DEV Community: Om Prakash

GEOmind 2:38 demo + 60s reel — get cited where buyers ask AI, or your money back

Om Prakash — Sat, 02 May 2026 13:36:50 +0000

GEOmind 2:38 demo + 60s reel — get cited where buyers ask AI, or your money back

Sixty percent of buyer research now starts in an AI chat, not Google. We rebuilt GEOmind so your brand shows up there — and put a money-back guarantee behind it.

Watch

Full demo (2:38, 1080p): https://youtu.be/r6n5oJ89kZM

60-second reel (9:16, vertical): https://youtu.be/yX9HE0z0igk

Every feature is captured live in the actual product. No slides. No mockups.

What's in the demo

Time	Feature
0:18	Onboarding — domain + ICP profile drives a buyer-intent prompt set, not generic queries
0:36	Hourly tracking dashboard — ChatGPT, Perplexity, Gemini, both raw and ICP-weighted share-of-voice
0:49	Action plan — names the specific Reddit threads / review sites your competitors are winning, plus a 4-week roadmap
1:03	Pre-publish predictor — paste a draft, get a 0–100 citability score across 10 factors before you ship
1:14	Multimodal brand audit — ChatGPT Vision view of your homepage, with a list of what AI cannot extract
1:26	Visual GEO — image-search readiness using the same retrieval ChatGPT Vision and Pinterest Lens use
1:38	ACP feed manager — paste your Shopify URL, get a feed your storefront can serve to ChatGPT Shopping
1:48	Hindi + Hinglish prompt set, festival-aware, city-keyed
2:00	Auto-fix — generates schema.org JSON-LD, llms.txt, alt-text, OG tags. Honest copy-paste delivery (we don't have your CMS yet)
2:17	Pricing — 2x cheaper than every rival at every tier
2:30	Try the live demo — geomind.app/track.html?demo=1 (no signup)

Pricing (INR + USD)

Tier	INR	USD	vs cheapest rival
Starter	₹1,199/mo	$14/mo	Otterly $29
Growth	₹3,699/mo	$44/mo	Lucid $89
Pro	₹19,999/mo	$244/mo	Otterly Premium $489

The outcome guarantee

If your blended share-of-voice doesn't lift by 5 percent in 30 days, half your credits refund automatically. No support ticket. No fight. The threshold is encoded; the refund is automated. We carry the execution risk.

Why we re-shot the video three times

The first cut had hardware jargon (4-GPU / Qwen / CLIP-BGE / RTX) that meant nothing to a buyer. The second cut had a 0:09–0:11 silence where the voice-over had ended but the scene hadn't. The third cut used a 145-bpm "viral tiktok" MusicGen track that produced harsh artefacts and was unlistenable. Iterations: trimmed jargon to buyer language, extended the hook VO so it spans the full scene, regenerated the BGM with a softer 110-bpm indie-pop prompt. All audio was generated by PixelAPI MusicGen — zero copyright risk, fully ours.

Try it (no signup)

Live demo dashboard: geomind.app/track.html?demo=1
Pre-publish predictor: geomind.app/predict.html
Sign up: geomind.app

Built in Hyderabad. Made for Indian D2C and global SaaS founders who want to be in the answer.

GEOmind 2.0: per-engine AI visibility, auto-fix via PixelAPI, and an ACP feed manager — for ₹999/mo

Om Prakash — Sat, 02 May 2026 06:36:31 +0000

GEOmind 2.0: per-engine AI visibility, auto-fix via PixelAPI, and an ACP feed manager — for ₹999/mo

A year ago we shipped GEOmind as a static HTML scanner — point it at a URL, get a 0–100 score on whether your structured data, alt text, llms.txt, and AI-crawlability allow you to be picked up by ChatGPT, Perplexity, Gemini and the other generative engines. It was useful but it was measuring inputs, not outcomes. We didn't tell you whether ChatGPT actually cited you. We just told you whether it could.

Two things happened in the last six months that made the input-only product no longer enough.

What changed

1. The market validated the outcomes layer. Profound raised $96M Series C at a $1B valuation in February 2026. AthenaHQ is at $295/mo with a YC seed. Lucid Engine is at $89/mo with paid Indie Hackers distribution. Peec.ai pulled $21M Series A. Every winning GEO platform now does the same thing: query the AI engines on the customer's behalf, track citation share over time, alert on competitor displacement. Static scanning alone is no longer table stakes.

2. ChatGPT shopping went live and then pivoted. OpenAI launched Instant Checkout with Etsy and over a million Shopify merchants in September 2025. In March 2026 they pivoted to product discovery — merchants now share product feeds via the Agentic Commerce Protocol (ACP), ChatGPT recommends products in answers, and customers go to the merchant's checkout. Almost no tool is helping merchants generate ACP-compliant feeds yet.

So we rebuilt.

What's live as of today

Per-engine visibility tracking

Every Monday at 02:00 UTC, GEOmind's daemon queries ChatGPT, Perplexity, Gemini, and Claude with your seed keywords and three buyer-intent prompt templates per keyword. It scores: were you mentioned at all? Were you in the top 3? Top 1? What competitors got cited instead? How did sentiment about you change over the last 30 days?

The result is the kind of dashboard most GEO tools charge $295/mo for, available on our ₹2,499/mo Growth plan and live-demoable for free at geomind.app/dashboard.html (rate-limited to 5 ad-hoc scans/IP/day).

You can run a sync demo right now: enter your domain + a keyword, and you'll get per-engine results in ~30 seconds. We send the queries from our keys, parse the responses, score visibility, extract competitor names. No "wait 7 days for your first report."

Auto-fix via PixelAPI

Most GEO tools tell you what's wrong and stop there. We're the only one that fixes it.

When the static scanner finds 17 issues — missing alt text, missing schema.org JSON-LD, no llms.txt, low-quality images — clicking the Auto-fix button hands the job to PixelAPI, our sister product. PixelAPI's VLM regenerates alt text. Its text-gen produces the schema.org JSON-LD. Its sitemap-crawler builds your llms.txt. Its image tools regenerate low-resolution product photos.

Costs typically 8–50 PixelAPI credits per scan-and-fix cycle. Growth and Pro tiers include monthly credits. The cross-product moat is something none of the standalone GEO tools (Profound, Athena, Lucid, Otterly, Peec) can match without buying an AI-tools company.

ACP Feed Manager for Shopify D2C

Paste your Shopify URL, get a schema.org/Product JSON-LD feed back in seconds. Validates against Shopify's public products.json endpoint, converts to the format ChatGPT's discovery layer reads.

curl -X POST https://geomind.app/api/acp-feed/generate \
  -H 'Content-Type: application/json' \
  -d '{"shopify_url":"https://allbirds.com","max_products":50}'

Returns a clean array of products with name, description, image, price, availability, brand, and a stable url per product. v1 is on-demand; v2 (later this month) is continuous sync — every catalog change re-publishes the feed automatically.

Visual GEO scoring via SearchPixel

Text-only GEO is half the picture. Buyers also upload phone photos to ChatGPT and ask "where can I buy this?" If your product images aren't surfacing in CLIP-embedding-based retrieval, you're invisible to image search.

SearchPixel — our hybrid CLIP ViT-L/14 + BGE base search API — exposes the same retrieval primitives ChatGPT Vision uses. GEOmind Pro indexes your catalog with SearchPixel and scores per-product visibility in image search. No GEO competitor exposes this because none of them have the embedding stack.

Pricing

Free: 5 static scans/month + 5 ad-hoc AI-engine demos / IP / day
Starter — ₹999/mo (~$12): 1 tracked domain, 5 keywords, ChatGPT + Perplexity weekly
Growth — ₹2,499/mo (~$30): 4 engines, 20 keywords, 3 competitors, monthly auto-fix credits
Pro — ₹9,999/mo (~$120): 3 sites, daily tracking, ACP Feed Manager, Visual GEO, citation autopublisher

INR via PayU (with GST invoice for Indian customers). USD via PayPal for everyone else. Same carry-forward-on-recharge policy as PixelAPI: top up before expiry and your unused tracking days roll forward.

Try it

geomind.app/dashboard.html — paste your domain + a keyword, hit Run. ~30 seconds for a real per-engine result. We use our own ChatGPT/Perplexity/Gemini/Claude keys for the live demo so there's nothing to set up.

If you're an Indian D2C founder running a Shopify store, the ACP feed manager + Visual GEO + Hindi-aware auto-fix combination is the bundle nobody else ships. We built it for you because we are you — same office, same rupee bills, same GST returns.

Hostile bug reports especially welcome. om@pixelapi.dev.

GEOmind Now Queries Real AI Models — See How ChatGPT, Perplexity, and Google AI See Your Brand

Om Prakash — Sat, 02 May 2026 04:02:27 +0000

GEOmind Now Queries Real AI Models — See How ChatGPT, Perplexity, and Google AI See Your Brand

Most GEO tools just scan your website's HTML. They check robots.txt, meta tags, and structured data. That's useful — but it doesn't answer the real question: "When someone asks an AI about my product category, does it recommend me?"

Today we're shipping the feature that answers that question.

What Changed

GEOmind now queries real AI models — ChatGPT, Perplexity, and Google Gemini — to see how they represent your brand. For each model, we ask 5 questions:

"What are the best products similar to [your brand]?"
"What is the best service for what [your brand] offers?"
"What is [your brand]? Is it good?"
"Which alternative would you recommend?"
"How much does [your brand] cost?"

We then analyze each response for:

Whether your brand is mentioned at all (40% of score)
Whether you're the top recommendation (35%)
Sentiment — positive, neutral, or negative (20%)
Competitive position — how many competitors are mentioned (5%)

Each engine gets a separate score (0-100), so you can see exactly where you're winning and where you're invisible.

Why This Matters

LucidEngine charges $89/mo for per-engine AI visibility tracking. GEOmind does it for $9/mo. But pricing isn't the point — the point is that most businesses have a blind spot.

Here's what we've seen in testing:

A Shopify store with 78/100 GEO score was completely invisible on ChatGPT (score: 12/100)
A DTC brand ranked #1 on Perplexity for their category but wasn't mentioned at all by Google AI
Two competitors were being recommended instead of the brand owner on every single engine

Static SEO scores don't catch this. Only actual AI model querying does.

What You Get

Per-Engine Scores: Separate 0-100 scores for ChatGPT, Perplexity, and Google AI.

Sentiment Tracking: See how each model describes you — are you "the best," "a good option," or "okay"?

Competitor Detection: See exactly which competitors AI recommends instead of you, and track their mention count across all 3 engines.

Gaps Identification: GEOmind tells you exactly where you're falling short — "ChatGPT doesn't mention you at all," "Perplexity recommends [Competitor] instead of you."

Historical Tracking: All scans are saved. Watch your scores change over time as you optimize.

How To Try It

Go to geomind.app
Paste any URL
Get your static GEO score (always works, free, no signup)
Configure API keys in the dashboard to unlock per-engine AI scoring
See the full picture — static analysis + actual AI model behavior

The Technical Stuff

We query 5 questions per engine, 3 engines = 15 API calls per scan. Each response is analyzed for brand mentions, sentiment, recommendation status, and competitor landscape.

The analysis is deterministic (no ML models, no fuzzy matching) — we use text search for brand mentions, pattern matching for "top recommendation" detection, and sentiment word counting for tone analysis.

API keys are stored per-user (encrypted in transit) and never shared. You can also set system-wide keys via environment variables.

What's Next

Alert triggers: Get notified when your scores drop or a competitor starts gaining mention share
Protocol Sync: One-click optimization for the surfaces AI reads — homepage, docs, pricing, reviews
Historical sentiment graphs: Visualize how AI perception of your brand changes over time

GEOmind is the most affordable GEO tool at $9/mo — 2x cheaper than Otterly, 10x cheaper than LucidEngine. Based on KDD 2024 research. Now with real AI model querying.

We shipped a hybrid CLIP+BGE search API in a weekend — and caught it auth-broken before launch

Om Prakash — Fri, 01 May 2026 15:00:43 +0000

We shipped a hybrid CLIP+BGE search API in a weekend — and caught it auth-broken before launch

There's a category gap most Indian D2C brands quietly suffer from. You set up your Shopify or WooCommerce store. The default search is keyword-only and unforgiving — "red silk wedding saree" doesn't surface a product titled "Banarasi gold zari kameez" even when that's exactly what the customer means. Algolia NeuralSearch starts north of ₹35,000/month. Doofinder's text-only tier is cheaper but adds zero visual or semantic understanding. OpenSearch is a build-it-yourself project that needs an EC2 GPU instance you don't want to manage.

So we built SearchPixel over the last few days. It's a single REST endpoint that takes a query — text or an image — and returns a ranked list of matching products. It runs on our own RTX 6000 GPU, costs us roughly nothing per request because the GPU is amortized across other PixelAPI workloads, and the open beta is free for the first 50 stores.

This post is the honest version of what we shipped, what we tried to ship and almost sent out broken, and where it goes next.

What's actually in the box

Two embedding models, run side by side:

Model	Job	Library	Vector dim
CLIP ViT-L/14 (`datacomp_xl_s13b_b90k`)	Image and short-phrase embeddings	`open_clip_torch`	768
BGE base (`BAAI/bge-base-en-v1.5`)	Long-form text embeddings	`transformers`	768
ChromaDB 1.5.8	Vector store	`chromadb`	—

Each store gets two ChromaDB collections — one CLIP, one BGE. A query goes to both, the per-collection scores are min-max normalised, and the final score is a 0.5/0.5 mix. The result, on the demo catalog of 12 sample SKUs, looks like this:

"saree bridal red"          → Saree Gold Silk Wedding Party Traditional   0.685
"formal leather shoes office"→ Leather Shoes Brown Formal Office          0.829
"kurti summer cotton"       → Kurti Pink Floral Printed Summer            0.778
"red outfit for a wedding"  → Men's Cotton Kurta Red Traditional          0.808

A pure keyword search would miss most of those — none of the queries are substrings of the product titles.

The bug we almost shipped

Honest part. The first version that was handed to me to validate "worked" — it returned 200 OK, the search results were relevant, the agent who built it sent over a tidy report saying "VERIFIED WORKING ✅".

It wasn't.

The auth dependency in main.py had this shape:

def get_store(x_api_key: str = Header(None)) -> str:
    if not x_api_key:
        return cfg.DEMO_STORE
    return x_api_key.split("_")[0] if "_" in x_api_key else x_api_key

That looks fine on a quick read. It's not. It means:

Sending no key at all silently routes you to the demo store. Read endpoints behaving this way is fine. Write endpoints behaving this way is not. I sent a POST /index with no auth and a body containing {product_id: "hack", name: "injected", description: "poisoned"}, and got a 200 back. The poison product showed up in the next public query.
Even with a key, any string was accepted — there was no check against an actual customer database. X-API-Key: acme_anything would have given the caller full control of the acme store.

The fix was to swap the dependency for one that asks Postgres whether the key is real:

async def auth_ctx(authorization=Header(None), x_api_key=Header(None)) -> AuthCtx:
    key = _extract_bearer_or_x_api_key(authorization, x_api_key)
    if not key:
        return AuthCtx(is_demo=True, user_id=None, store_id="demo", plan="demo")
    user = await _user_by_key(key)        # 60s TTL cache → asyncpg → users.api_key
    if not user:           raise HTTPException(401, "Invalid API key")
    if user["blocked"]:    raise HTTPException(403, "Account suspended")
    return AuthCtx(is_demo=False, user_id=user["id"], store_id=f"u{user['id']}", ...)


async def require_real_user(ctx: AuthCtx = Depends(auth_ctx)) -> AuthCtx:
    if ctx.is_demo:
        raise HTTPException(401, "API key required for write operations.")
    return ctx

/search (read) takes auth_ctx — demo OK. /index, /index/image, /product/<id> DELETE, /stores all take require_real_user. The demo store_id is reserved — even with a valid key you can't write into it, because your store_id comes from f"u{user.id}", not from anything user-supplied.

Eleven pytest tests now pin this contract. Three of them probe the bug class above directly:

test_index_without_key_is_rejected — no X-API-Key header → 401
test_index_with_invalid_key_is_rejected — X-API-Key: garbage_key_does_not_exist → 401
test_cross_tenant_isolation — user A indexes a uniquely-phrased product, user B searches for that phrase with their own key, asserts the product is not in the results

If any of those go red on the next deploy, the regression is impossible to miss.

What I dropped from the launch copy

The first cut of the landing page promised "₹4,999/mo · Razorpay built-in · 14-day free trial". There was no Razorpay code in the repository. There was no billing endpoint. There was no users-table integration on the SearchPixel side beyond the auth dependency I just described. Promising those on the landing page would have been the marketing equivalent of return 200 for POST /index — looks fine, isn't.

So pricing on the live page now says "Free in beta", the CTA is a mailto:om@pixelapi.dev, and the Razorpay row in the comparison table got swapped for "Self-hosted GPU (no cloud markup)" — which is a thing we actually do.

When we put a real price on it, current beta users will get a discount and a heads-up, not a surprise card charge.

Where this came from

SearchPixel sits inside PixelAPI, the same project that already runs portrait restoration, virtual try-on, pattern generation, and a handful of other AI endpoints on a small fleet of GPUs in our office. The PixelAPI gateway already has an auth model, a usage table, a Razorpay subscription flow, a Zoho Books integration. Reusing that machinery — same key, same dashboard — was strictly cheaper than rebuilding any of it.

That decision is also why this is actually serviceable: SearchPixel is just one more endpoint family to operate, not its own SaaS with its own on-call schedule.

What's left

Shopify and WooCommerce plugins. The REST API works today; the one-line embed is on the bench.
A /search query parameter for category and price filters. The data is in ChromaDB metadata; the route layer just doesn't expose it yet.
Visual search via uploaded image (POST /search with query_image) is implemented but underexercised — the path needs a real test catalog, not just 12 demo SKUs.
Honest pricing. We'll publish a number when we know what the steady-state GPU cost looks like in production. Beta users will find out before anyone else.

Try it

Live at searchpixel.pixelapi.dev — the homepage demo runs against the public catalog, no key needed. Docs at /docs. For a real index of your own, reply to this post or email om@pixelapi.dev with your store URL and platform — we'll send back an API key (or pair it with the PixelAPI key you already have) within a working day.

Bug reports welcome. Hostile bug reports especially welcome.

I built a textile pattern generation API because PatternedAI has no API

Om Prakash — Thu, 30 Apr 2026 05:02:38 +0000

I built a textile pattern generation API because PatternedAI has no API

There's a real category gap in the AI-pattern space.

PatternedAI has 600K users. Spoonflower's design tools are everywhere. Both are excellent GUIs for textile designers. Neither has a public REST API. So if you're a print-on-demand shop, a Shopify store auto-generating colorways, or an indie game studio that needs seamless fabric textures — you're stuck either copy-pasting through a web UI or paying enterprise rates for a custom integration.

I shipped PixelAPI's /v1/pattern endpoint yesterday — 8 styles, 512px or 1024px output, recolor + upscale ops, fully seamless tileable. At $0.008/pattern, it's 2-5× cheaper than PatternedAI's GUI sessions.

This isn't a "Show HN, please clap." This is the story of what almost shipped at 2/10 quality, why I caught it before customers did, and the open-source-only tooling that got us to 8.4/10 average.

What's in the box

# 1. Generate
curl -X POST https://api.pixelapi.dev/v1/pattern/generate \
  -H "Authorization: Bearer YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "style": "ikat",
    "resolution": "512",
    "prompt": "indigo and cream traditional ikat textile"
  }'
# → {"generation_id":"...","credits_used":8,"poll_url":"/v1/pattern/{id}"}

# 2. Poll
curl https://api.pixelapi.dev/v1/pattern/{id} \
  -H "Authorization: Bearer YOUR_KEY"
# → {"status":"completed","output_url":"https://api.pixelapi.dev/outputs/.../1495e592...png"}

# 3. (Optional) recolor a copy
curl -X POST https://api.pixelapi.dev/v1/pattern/recolor \
  -d '{"source_url":"...","hue_shift":180}'
# → 2 credits, hue rotation in HSV

# 4. (Optional) upscale to 2048px print-ready
curl -X POST https://api.pixelapi.dev/v1/pattern/upscale \
  -d '{"source_url":"..."}'
# → 3 credits, Lanczos

The 8 styles + the model behind each

Style	Model	Why
Floral	PatternDiffusion	SD2 fine-tuned on 6.8M tileable patterns; ditsy-print sweet spot
Geometric	PatternDiffusion	Tessellation + grid prompts
Ikat	PatternDiffusion	Traditional Indian woven patterns
Paisley	PatternDiffusion	Boteh motif training data
Tribal	PatternDiffusion	Bold symmetrical Aztec-style
Animal-print	PatternDiffusion	Leopard/zebra texture repeat
Abstract	SDXL-seamless	Free-form abstract benefits from SDXL's broader training
Stripes	PIL algorithm	See below — this one almost destroyed me

Why "stripes" needed an algorithm and not an AI

Both PatternDiffusion and SDXL-seamless failed at clean parallel stripes during my QC audit. PatternDiffusion produced rainbow plaid noise. SDXL-seamless produced "shirt motifs" because it saw "shirt" in the prompt. Neither model was trained on enough plain-stripe samples to handle a request as simple as "navy blue and white classic shirt vertical stripes."

Spending 4 hours iterating prompt engineering on something Pillow does in 10 lines made no sense. So:

# /home/om/pixelapi-worker-code/models/pattern_model.py
def synthesize_stripes(width=512, height=512, prompt=""):
    """Algorithmic stripe / plaid / gingham. Zero VRAM, deterministic."""
    p = prompt.lower()
    is_horizontal = "horizontal" in p
    is_plaid = any(k in p for k in ("plaid","tartan","gingham","check"))
    stripe_w = 6 if "thin" in p else 36 if "thick" in p else 18

    # Pull color words from the prompt
    colors = [color_table[k] for k in color_table if k in p]
    if not colors:
        colors = [(10,30,90), (255,255,255)]  # navy/white default

    img = Image.new("RGB", (width, height), colors[0])
    draw = ImageDraw.Draw(img)
    for x in range(0, width, stripe_w * 2):
        if is_horizontal:
            draw.rectangle([0, x, width, x + stripe_w], fill=colors[1])
        else:
            draw.rectangle([x, 0, x + stripe_w, height], fill=colors[1])
    if is_plaid:
        # ... overlay perpendicular stripes
    return img

Result: 10/10 quality, 10ms generation, zero GPU usage. A user asks for "navy blue and white classic shirt vertical stripes" and gets exactly that, every single time. A user asks for "red green plaid tartan" and gets a clean tartan. Diagonal "yellow and black warning stripes" works the same way.

The lesson — and one I want to underline because it's the part nobody talks about: AI is overkill for half of what people use AI for. Pattern recognition is not the same as pattern generation. SDXL is a 7B-parameter monster that's wasting electricity to render content that a Bresenham line algorithm could produce in nanoseconds.

The QC ladder that caught the silent failures

Generating bad output is one thing. Charging customers for it is the bigger sin.

The endpoint sits behind a structural-QC gate that runs on every output:

Pass-through detection: if the output is pixel-equivalent to the input (when there's an input at all), reject. (Caught a real case where a remove-text job returned the input unchanged and was billed as "completed.")
Scene-destruction detection: if more than 35% of pixels changed for an edit operation, reject (catches FireRed-style hallucinations where the model replaces the subject entirely).
VLM verification: a Qwen2.5-VL-7B QA pass that compares input + output + prompt and emits a good/bad/unsure verdict.

When any gate fails, the job goes through an iteration ladder (up to 5 attempts with different prompt strategies / fallback models). After exhaustion: automatic refund of credits, no email asking the customer to fight for it.

The two recolor jobs I broke during a refactor today were caught by an integration test before any customer saw them; the underlying bug was a missing "operation": "recolor" flag in the redis params dict, which made the worker's dispatch logic fall through to the "generate" branch. Five-minute fix; a customer would have seen unrelated pattern output instead of a hue shift.

Pricing reality check

Operation	Credits	USD	PatternedAI/competitor
512px generate	8	$0.008	$0.015–0.045 (GUI only)
1024px print-ready	15	$0.015	$0.030–0.090 (GUI only)
Recolor (HSV hue shift)	2	$0.002	N/A — no API competitor
Upscale to 2048px	3	$0.003	N/A — no API competitor

There's also no per-seat licensing, no monthly minimum, no 30-day deprecation cycles. Pay-per-use, period.

What's NOT 10/10 yet (honest list)

Style overlap: pattern-diffusion sometimes drifts toward "tribal" when asked for plain "geometric." Workaround: include "minimalist scandinavian" or "two-tone" in the prompt. The default style hint now does this for you.
Romanized non-English prompts: if you write "kuradedan" (Hindi-in-Latin for "trash bin") instead of native Devanagari "कूड़ेदान," the langdetect-based translator can't recognize it and the model gets the romanized string. The QC ladder catches the bad output and refunds, but you lose 5 minutes.
Single-character motif requests ("just one big paisley") aren't this endpoint's job — try the regular /v1/image/generate instead.

Try it

100 free credits on signup at pixelapi.dev. That's 12 generations, or 50 recolors, or 33 upscales — enough to validate the API for your use case.

If you find a generation under 8/10, hit reply on the auto-email. The QC ladder will already have refunded you, and the failure case becomes the next prompt-engineering iteration here.

— posted from a 24h debugging-and-shipping run; corrections welcome.

The Real Cost of AI Product Photography in 2026: Why You're Overpaying by 10x

Om Prakash — Mon, 27 Apr 2026 06:12:39 +0000

If you've been building e-commerce automation tools in the past two years, you've probably noticed something frustrating: AI image processing pricing is all over the place.

Remove.bg charges $0.20 per background removal. Photoroom's API starts at $15/month just to get started. And if you want video generation? $0.05 per second on Runway.

For a small e-commerce store processing 10,000 product images a month, that's $2,000 in API costs alone — before you even factor in hosting, development, or marketing.

But here's what the pricing pages don't tell you: The underlying GPU compute costs have dropped 10x in 18 months. The cloud providers are still charging premium prices while the infrastructure costs have collapsed.

The Infrastructure Revolution Nobody Talks About

Running AI image models has become remarkably cheap. The same RTX 6000 Ada GPU that cost $8,000 a year ago can now process:

1,000 background removals in under 60 seconds
500 AI-enhanced product photos in under 5 minutes
10 AI-generated videos (Wan 2.1) in under 10 minutes

On bare-metal infrastructure with no cloud markup, that compute costs roughly $0.0005 per background removal and $0.017 per video second — 40x and 3x cheaper than the leading commercial APIs respectively.

What This Means for Developers

If you're building:

An e-commerce listing automation tool
A Shopify/WooCommerce image plugin
A bulk product photography service
Any app that processes product images at scale

You're leaving money on the table if you're using premium APIs. The quality gap between "premium" AI image processing and open-source models running on optimized GPU infrastructure has essentially closed in 2025-2026.

How PixelAPI Does It

At PixelAPI, we run RTX 6000 Ada and RTX 4070 GPUs on our own bare-metal network. No AWS markup. No Google Cloud premium. Just direct GPU access at infrastructure cost.

Our pricing:

Background Removal: $0.001 per image (vs Remove.bg's $0.20)
Image Generation (FLUX.1-schnell): $0.0012 per image
AI Video (Wan 2.1): $0.017 per second (vs Runway's $0.05)
Background Replace: $0.0025 per image

All plans include at least 1,000 free credits to test.

The API is dead simple:

import requests

response = requests.post(
    "https://api.pixelapi.dev/v1/remove-background",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    files={"image": open("product.jpg", "rb")}
)

result = response.json()
print(result["output_url"])  # Your processed image

The Bottom Line

AI image processing infrastructure costs have dropped 10x. Premium API pricing hasn't caught up. If you're building products that rely on image processing, you owe it to yourself (and your customers) to benchmark against the new generation of cost-optimized APIs.

Your compute costs shouldn't be the thing that kills your startup.

API Docs: https://pixelapi.dev
Free Credits: Sign up at https://pixelapi.dev — no credit card required.

AI Video Generator: Crafting Engaging Content with PixelAPI

Om Prakash — Fri, 24 Apr 2026 06:35:29 +0000

As an AI image, video, and audio generation API, PixelAPI’s AI Video Generator is a powerful tool that leverages WAN 2.1 to transform simple text inputs into high-quality videos. This capability opens up new possibilities for content creators, developers, and anyone looking to enhance their projects with dynamic visual storytelling.

Real-World Use Cases

Content Creator: Explainer Videos

Content creators often face the challenge of producing engaging yet informative explainer videos without a large budget or access to professional video editors. With PixelAPI’s AI Video Generator, creating these videos becomes both efficient and cost-effective.

Let's consider an example where a content creator is making an explainer video for a tech startup's new feature: real-time chat integration within web applications. Using the API, they can input text like:

Title: "Real-Time Chat Integration for Web Applications"
Text:
- "Imagine having instant communication with your users right in their browser."
- "Our real-time chat feature allows you to connect and engage effortlessly."
- "Try it now and see the difference!"

The API processes this input, generating a video that combines these text inputs into an animated scene, complete with engaging visuals and voiceovers. The result is a polished explainer video that effectively communicates the product's value.

Developer: Prototyping Video Features

For developers working on video projects or apps that require visual demonstrations of features, PixelAPI’s AI Video Generator can be a game-changer. It enables quick prototyping without the need for extensive animation skills or high-end software tools.

Suppose a developer is working on an app that allows users to create custom avatars with various hair styles and accessories. By inputting descriptive text such as:

Title: "Custom Avatar Creator"
Text:
- "Select your avatar's hairstyle."
- "Add a pair of stylish sunglasses."
- "Customize the background scene for your avatar."

The API generates a video that demonstrates these features in action, allowing the developer to quickly iterate on the user interface and functionality.

Social Media Reels Creation

Social media platforms thrive on engaging, bite-sized content. For creators looking to produce high-quality social media reels without the time or skill required for traditional video production, PixelAPI’s AI Video Generator offers a streamlined solution.

A perfect scenario might be creating a series of fun, educational posts about popular tech trends. By inputting text like:

Title: "Tech Trends 2023"
Text:
- "Blockchain and cryptocurrencies explained."
- "The future of virtual reality in gaming."
- "Stay ahead with the latest AI developments!"

The API generates animated reels that capture attention, making them ideal for sharing on platforms like TikTok or Instagram.

Technical Workflow

To use PixelAPI’s AI Video Generator, follow these steps:

Sign Up and Obtain API Key: Register for an account and obtain your API key.
Define Your Text Inputs: Craft the text that will form the basis of your video. Ensure clarity and conciseness to make the best use of the tool.
Make API Requests: Use a simple HTTP request or integrate with the API via SDKs available for popular programming languages like Python, JavaScript, etc.

Here’s an example using Python:


python
import requests

api_key = 'YOUR_API_KEY'
text_inputs = [
    "Title: 'Tech Trends 2023'",
    "Text: ['Blockchain and cryptocurrencies explained.', 'The future of virtual reality in gaming.', 'Stay ahead with the latest AI developments!']"
]

url = 'https://api.pixelapi.com/video/generate'

headers = {
    'Authorization': f'Bearer {api_key}',
    'Content-Type': 'application/json'
}

response = requests.post(url, json={'inputs': text_inputs})

if response.status_code == 20

Stitching Video Snippets Together Seamlessly with AI

Om Prakash — Fri, 24 Apr 2026 06:29:48 +0000

If you’ve ever worked on a project that required more than just a single piece of footage—say, compiling a product demo from five different angles, or assembling a highlight reel from dozens of raw clips—you know the headache of manual video editing. You spend time syncing audio, trimming rough cuts, and making sure the transitions feel natural.

That’s where the ability to programmatically merge and stitch multiple video clips using AI becomes incredibly useful. We’ve been playing around with the Video Merger functionality within PixelAPI, and it's really streamlined the process of taking disparate video segments and weaving them into a cohesive final product, all through an API call.

For developers building applications that deal with visual media, this capability moves video assembly from a manual, time-consuming task to a reliable, scalable backend function.

The Problem with Manual Assembly

Think about an e-commerce scenario. A brand might film a product demonstration across three different locations: the studio setup, the 'in-use' environment, and a close-up detail shot. Instead of having an editor stitch these together, you need a system that can take the three separate video files and combine them in a specific sequence, perhaps adding a standardized fade or cut between them.

If you're building a backend service for video content creation—maybe for a news aggregator or a sports recap site—you are dealing with batch processing. You don't want to write a separate script for every single combination of clips. You need a function that says, "Take these 10 files, stitch them in this order, and give me one output."

How the Video Merger Works Under the Hood

From a developer standpoint, the appeal here is the abstraction. Instead of worrying about FFmpeg command-line intricacies, codec compatibility across various inputs, or complex timeline management, you feed the API a list of inputs and the desired structure, and it handles the heavy lifting of merging and stitching. The AI aspect helps ensure that the resulting output isn't just a jarring concatenation of files, but a more polished assembly.

Let's look at a simple Python example of how you might queue up a few clips to create a short compilation.


python
import requests
import json

def stitch_video_compilation(clip_paths: list, output_name: str):
    """
    Sends a request to the Video Merger endpoint to combine multiple clips.
    """
    api_endpoint = "https://api.pixelapi.com/v1/video/merge" # Placeholder endpoint

    payload = {
        "inputs": clip_paths,
        "output_format": "mp4",
        "transition_style": "crossfade" # Example of an AI enhancement setting
    }

    headers = {
        "Authorization": "Bearer YOUR_API_KEY",
        "Content-Type": "application/json"
    }

    print(f"Sending request to merge {len(clip_paths)} clips...")

    try:
        response = requests.post(api_endpoint, headers=headers, data=json.dumps(payload))
        response.raise_for_status()

        result = response.json()
        print("Merge job submitted successfully.")
        print(f"Job ID: {result['job_id']}")
        return result['job_id']

    except requests.exceptions.RequestException as e:
        print(f"An error occurred during the merge process: {e}")
        return None

# --- Example Usage ---
# Assume these paths point to local or accessible video files
clip_list = [
    "/path/to/intro_shot.mp4", 
    "/path

Boosting Image Quality for Production Workflows with 4x Upscaling

Om Prakash — Fri, 24 Apr 2026 06:28:26 +0000

When you're building applications that deal with visual media, one of the most common headaches is resolution mismatch. You might have a beautiful, high-fidelity asset, but the source material is too small, or perhaps you've generated something amazing with an AI model, only to find the output isn't quite large enough for the final print or high-DPI display.

I’ve found that relying on simple upscaling methods often leads to that tell-tale "watercolor" effect—blurry, soft, and lacking fine detail. That’s where the 4x Upscaler capability in the PixelAPI really shines. It’s not just about making the pixels bigger; it’s about intelligently reconstructing the missing detail, which is crucial when the final output needs to look professional, whether it's for e-commerce listings or personal keepsakes.

The Problem with Downscaling and Upscaling in Practice

Let’s talk about a real scenario. Imagine you're building an e-commerce platform. You’re sourcing product photography from various vendors. Some send you perfect, massive TIFF files, but others send you tiny, optimized thumbnails (say, 200x200 pixels) that are fine for a website grid view but completely useless if a customer wants to print a detailed product shot or view it on a large promotional banner.

If you just scale that 200x200 image up by 4x using basic interpolation, you get a 800x800 image that looks noticeably soft. The texture of the stitching on a leather bag, or the fine grain on a piece of jewelry, gets lost in the averaging process.

The 4x Upscaler handles this by analyzing the existing data and predicting what the high-frequency details should look like at a higher resolution. It’s a significant step up from simple pixel stretching.

Developer Workflow: E-commerce Asset Preparation

For developers integrating this into a backend pipeline, the workflow is straightforward: ingest the low-resolution asset, pass it through the upscaler endpoint, and then use the resulting high-resolution image for the final delivery format.

Here’s a conceptual look at how you might structure this using a simple API call pattern (assuming you are using a language like Python):

import requests
import base64

# Assume 'low_res_image_bytes' is the raw bytes of the 200x200 product thumbnail
# Assume API_KEY and API_ENDPOINT are configured

headers = {"Authorization": f"Bearer {API_KEY}"}

# The payload structure depends on the specific endpoint design, 
# but conceptually, you send the image and specify the scaling factor.
payload = {
    "image_data": base64.b64encode(low_res_image_bytes).decode('utf-8'),
    "scale_factor": 4
}

response = requests.post(API_ENDPOINT, headers=headers, json=payload)

if response.status_code == 200:
    upscaled_image_bytes = response.json()["upscaled_image"]
    # Now you have a high-fidelity 800x800+ image ready for the client or print service
    with open("high_res_product.png", "wb") as f:
        f.write(base64.b64decode(upscaled_image_bytes))
    print("Successfully upscaled asset.")
else:
    print("Upscaling failed.")

The key takeaway here is that the output quality allows you to maintain the perceived fidelity of the original source material, even when you need it significantly larger

Flipping Product Photography: How to Seamlessly Change Backgrounds with AI

Om Prakash — Fri, 24 Apr 2026 06:21:57 +0000

If you work in e-commerce, you know the pain point. You get a great shot of a product—say, a beautiful pair of sneakers—but the background is an unappealing mess: a cluttered kitchen counter, a patch of patchy grass, or just a distracting wall. Getting consistent, professional product photography across hundreds of SKUs is a massive headache, and traditional studio setups are expensive and slow.

That’s where background replacement comes in handy. We’ve been playing around with an AI image generation API that handles this task, and honestly, it’s been a genuine workflow accelerator for my personal projects and for simulating some e-commerce pipelines.

At its core, the tool takes an input image, intelligently masks the subject (the sneakers, in our example), and then lets you swap out the entire background with something completely new—whether that's a pristine white void, a tropical beach, or a minimalist concrete setting.

The Technical Workflow

From a developer standpoint, the process is surprisingly straightforward, even though the underlying AI magic is complex. You are essentially performing a three-step process via API calls:

Upload/Identify: Provide the source image containing the subject.
Prompt/Define: Tell the API what you want the new background to be.
Generate: Receive the composite image.

Here’s a conceptual look at how you might structure this in Python, assuming you have the necessary API client set up.

import requests
import base64

# Assume API key and endpoint are configured elsewhere
API_ENDPOINT = "https://api.pixelapi.com/v1/background-replace"

def replace_background(product_image_path: str, target_scene_prompt: str, output_filename: str):
    """
    Sends an image and a prompt to the background replacement API.
    """
    try:
        with open(product_image_path, "rb") as image_file:
            image_data = image_file.read()

        headers = {
            "Authorization": "Bearer YOUR_API_KEY",
            "Content-Type": "application/json"
        }

        payload = {
            "source_image": base64.b64encode(image_data).decode('utf-8'),
            "target_prompt": target_scene_prompt,
            "output_format": "jpeg"
        }

        response = requests.post(API_ENDPOINT, headers=headers, json=payload)
        response.raise_for_status()

        # Assuming the response contains the base64 encoded resulting image
        result_data = response.json()
        if 'result_image' in result_data:
            base64_image = result_data['result_image']
            # Decode and save the final image
            img_bytes = base64.b64decode(base64_image)
            with open(output_filename, "wb") as f:
                f.write(img_bytes)
            print(f"Successfully generated and saved: {output_filename}")
        else:
            print("Error: Could not find result image in API response.")

    except requests.exceptions.RequestException as e:
        print(f"An API request error occurred: {e}")

# Example Usage:
# replace_background("sneakers_on_grass.jpg", "minimalist marble countertop with soft, natural lighting", "sneakers_marble.jpg")

Real-World Scenarios Where This Shines

This isn'

Cleaning Up Source Images: A Developer's Guide to Text Removal

Om Prakash — Fri, 24 Apr 2026 06:20:05 +0000

When you're building applications that rely on visual assets—whether it's e-commerce product catalogs, documentation screenshots, or complex UI mockups—you quickly run into a universal problem: messy source material.

Maybe you snagged a great reference photo from the web, but it has a faint "Image by [Blog Name]" watermark across the corner. Or perhaps you've taken a screenshot of a helpful diagram, but the system-generated timestamps and footer text obscure the key data points. If you can't get a clean base image, your downstream AI models, or even your front-end assets, will inherit that noise.

This is where the Text Remover capability in the PixelAPI stack becomes incredibly useful. It’s not just about erasing; it’s about intelligently discerning what is text and what is content, and then cleanly removing the former while preserving the underlying texture and structure of the image.

The Problem with Simple Blurring

I’ve found that simply trying to mask out text using basic image editing tools often leaves behind obvious artifacts—a blocky, unnatural patch, or a blurry smear that draws more attention than the text itself ever did. For developer workflows, this is a dealbreaker. You need the illusion of the text never having been there.

The approach I've found effective with the API is that it seems to understand the context of the removal. It doesn't just paint over; it reconstructs.

Workflow Deep Dive: Three Real-World Scenarios

To show you how this fits into actual development pipelines, I wanted to walk through three specific use cases where I’ve integrated this functionality.

1. E-commerce Catalog Prep (Watermark Removal)

Imagine you are building a platform that aggregates product imagery from various sources. Sometimes, the supplier images come with their branding or watermarks overlaid. If you plan to use these images for a client-facing catalog, those watermarks are distracting and unprofessional.

Instead of manually cropping or using overly aggressive touch-up tools, you can run the source image through the Text Remover endpoint.

Here’s a conceptual snippet of how this might look in a Python backend processing a batch of images:

from pixelapi import ImageProcessor

def clean_product_image(image_path: str, output_path: str):
    """Removes visible text/watermarks from a product image."""
    try:
        # Assuming the API call handles the heavy lifting
        processed_image = ImageProcessor.remove_text(
            input_file=image_path, 
            output_format="PNG"
        )
        processed_image.save(output_path)
        print(f"Successfully cleaned image saved to {output_path}")
    except Exception as e:
        print(f"Error processing {image_path}: {e}")

# Example usage loop in a batch job
# for file in list_of_supplier_images:
#     clean_product_image(file, f"cleaned/{file.split('.')[0]}.png")

The key here is that the API handles the complexity of identifying the watermark (which is often semi-transparent or uses complex fonts) and reconstructing the background texture underneath it.

2. Documentation & Knowledge Base Cleanup (Screenshot Sanitization)

This is perhaps the most common use case for developers. When documenting a tricky setup—say, a configuration panel in a third-party tool—you take a screenshot. But the screenshot inevitably includes the operating system's date/time stamp in the corner, or a "Help Center" banner at the bottom.

If you are feeding this screenshot into an LLM or an image recognition model for documentation purposes, that extraneous text

Cleaning Up Imperfections: Seamless Object Removal with AI Inpainting

Om Prakash — Fri, 24 Apr 2026 06:18:49 +0000

If you've ever taken a perfect shot only to realize there's a distracting trash can in the foreground, or a random person walking through the background of your portrait, you know the feeling. Those little details can derail an otherwise great image, no matter how good your camera is. For developers building applications around visual media, this "cleanup" step is often a surprisingly complex hurdle. That's where scene-aware object removal tools come in, and I wanted to share how integrating this capability into a workflow can save a ton of post-production time for various industries.

The core concept here is object removal, but the trick isn't just "erasing" something. True object removal requires AI inpainting—meaning the system has to intelligently guess what should be underneath the object you're removing, filling in the missing pixels in a way that matches the surrounding texture, lighting, and perspective.

A Travel Photographer's Nightmare (and Solution)

I was working with a travel client who specialized in architectural photography. They often shoot crowded public squares, and while the architecture is stunning, the composition gets ruined by tourists leaning into the frame, stray dogs, or overflowing bins.

The old workflow was simple: shoot it, then spend hours in Photoshop manually cloning out every single unwanted element. This was slow and inconsistent.

By integrating an object removal API, the workflow changed dramatically. Instead of manual cloning, we pass the image and a mask (or just let the API detect the object) for the distracting element.

Imagine a shot of a beautiful Parisian facade. A group of tourists is standing directly in front of a detailed window grate. We feed the image and mask out the tourists. The AI doesn't just patch the background; it reconstructs the texture of the stone wall and the window pattern behind where the people were standing. The result is often indistinguishable from the original, clean shot.

Real Estate: De-Cluttering the Scene

Real estate photography has its own unique set of visual problems. A beautiful living room might look cluttered because the staging furniture has random knick-knacks on the coffee table, or maybe there's a power cord visible near the baseboard.

Our goal here isn't just to remove the power cord; it's to remove it and make the surface look like nothing was ever there.

If you're building a platform for property listing management, this is a massive time saver. Instead of forcing the photographer to spend time rearranging throw pillows just to hide a visible outlet, they can capture the room as-is, and the backend service cleans it up automatically.

The developer side of this is straightforward:

Input: Image file.
Masking (Optional): If the object is complex (like a specific chair leg), you generate a mask. If it's general clutter, you might let the API handle the detection.
API Call: Send the image and mask to the object removal endpoint.
Output: The cleaned image.

Example pseudo-code snippet for a backend service handling this:


python
import requests
from PIL import Image

def clean_room_image(image_path: str, mask_path: str = None) -> Image.Image:
    """Removes specified objects from an image using the object removal API."""

    api_endpoint = "YOUR_OBJECT_REMOVAL_API_URL"

    payload = {
        "image": open(image_path, "rb").read(),
        "mask": open(mask_path, "rb").read() if mask_path else None
    }

    headers = {"Authorization": "Bearer YOUR_API_KEY"}