DEV Community: Oleksii Sytar

Beyond Vibe Coding: Graph-Grounded Spec-Driven Development (GG-SDD)

Oleksii Sytar — Mon, 10 Aug 2026 10:22:46 +0000

A practical architecture for linking stakeholder intent, specifications, decisions, code, tests, and production evidence.

AI can write code quickly. That is no longer the interesting bottleneck.

The harder problem is keeping a changing product coherent: making sure a new request still respects the product model, architecture decisions, data contracts, operational constraints, and work already being done by other developers or agents.

This is where many AI-assisted projects break down. A feature is specified in one conversation, implemented in another, reviewed against a partial repository view, and deployed without updating the documents that future work will rely on. Each individual output can look convincing while the system as a whole drifts.

I have been developing a different operating model. I call it Graph-Grounded Specification-Driven Development (GG-SDD): a traceability-first implementation pattern within the broader AI-Driven Development Lifecycle, backed by a project-scoped graph, evidence-aware retrieval, explicit approval gates, and a closed loop from stakeholder intent to production verification.

GG-SDD is not a claim that specification-driven development, knowledge graphs, RAG, requirements traceability, MCP, or an AI-native lifecycle are new. They are not. The proposal is to make them work as one lean, project-level control system.

The central idea is simple:

Every change should be derived from a versioned map of the project, carry its source evidence and impact path into implementation, and write verified knowledge back after delivery.

Vibe coding optimizes a prompt. GG-SDD optimizes the continuity of the product.

The bottleneck moved from code generation to coherence

Vibe coding is useful. It is excellent for exploration, disposable prototypes, interface experiments, and short feedback loops where a person can continuously inspect the result.

It is a weak control model for a long-lived production system.

The limitation is not that the model cannot produce enough code. It is that the model usually sees a temporary and incomplete representation of the project. A prompt might include a ticket and a few files, but omit an architectural decision, a data migration constraint, a security rule, an incident history, or another specification being implemented in parallel.

Larger context windows do not remove this problem. Anthropic's guidance on context engineering treats context as a finite attention budget: the objective is not to load everything, but to select the smallest set of high-signal information that maximizes the likelihood of the desired result.[1]

The result is a shift in engineering work. As code generation becomes cheaper, context selection, semantic consistency, verification, and accountability become more important.

Specification-driven development is necessary, but not sufficient

Specification-driven development improves the situation by making intent explicit before implementation.

GitHub Spec Kit formalizes an agentic process that can include a project constitution, specification, clarification, technical plan, requirement checklist, implementation tasks, cross-artifact analysis, implementation, and convergence checks.[2] Kiro follows a related requirements–design–tasks workflow and combines it with persistent steering files and MCP integrations.[3]

This is a major improvement over an isolated prompt. A specification becomes a durable artifact, and quality gates can catch ambiguity before code is produced.

However, a set of good specification files is not automatically a coherent project memory.

Three problems remain:

Scope. A feature specification may not include the product decisions, cross-service dependencies, incidents, deployment rules, or work in progress that affect it.
Persistence. Specs, plans, tasks, and implementation can diverge unless the team explicitly chooses how changes flow back through the artifact set. GitHub's own Spec Kit documentation identifies silent divergence as a risk in mutable, flow-back models.[4]
Coordination. Two agents can follow the same template and still make contradictory choices if they retrieve different evidence or do not know that a shared decision has changed.

GG-SDD adds a persistent relationship model around the specification workflow.

The broader lifecycle already has a useful industry name. AWS introduced the AI-Driven Development Lifecycle (AI-DLC) in 2025, organizing work from business intent through inception, construction, and operations while maintaining context and human validation.[20] Its 2026 AIDLC Collaborative sample goes further: it models requirements, stories, designs, decisions, and code as typed graph artifacts; supports bidirectional traceability, impact analysis, MCP-based agent access, human gates, parallel work, and pull-request output.[21]

That is very close prior art. GG-SDD should therefore be understood as an independent, lightweight, cloud-neutral and traceability-first implementation pattern within AI-DLC — not as the invention of a new lifecycle.

What already exists

The industry is clearly converging on better context infrastructure.

Sourcegraph described Cody's context engine and graph context in 2023.[5] Aider uses a concise repository map and graph ranking to select important symbols within a token budget.[6] Augment and Qodo now use the term Context Engine for multi-repository code understanding, semantic retrieval, history, documentation, and agent workflows; both also expose context to other tools through MCP.[7][8]

Tabnine is an even closer commercial analogue. Its Enterprise Context Engine is described as a continuously updated knowledge graph of artifacts, decisions, incidents, ownership, and dependencies. Its Requirements Planning workflow ingests code, documentation, Jira, ADRs, and operational data, then generates an executable PRD, stories, acceptance criteria, testing plan, and deployment checklist linked back to components and files.[22]

Other products extend the idea beyond code. Factory Threads links discussions, code, and documents into shared dynamic context.[9] Allstacks describes a Context Graph connecting strategy, tickets, commits, and other delivery signals across the software lifecycle.[10] OneTrace versions relationships among PRDs, stories, acceptance criteria, tests, commits, and pull requests, then uses them to identify coverage gaps and drift.[11] Strata19 describes a typed project graph, living documentation, impact analysis, verification gates, and MCP queries.[12] SpecGraph combines brownfield discovery, versioned specification packs, MCP delivery to coding agents, pull-request citations, and a live amendment loop.[23]

Academic work points in the same direction. DraCo retrieves from a repository-specific context graph built from data-flow relations.[13] CodexGraph uses a code graph database as an interface between an LLM agent and a repository.[14] TraceLLM applies LLMs to requirements trace-link recovery while retaining human validation.[15] TraceDev, accepted at ISSTA 2026, uses a heterogeneous traceability graph as shared structured context and memory for requirement, design, development, testing, and validation agents.[24] Trust-Aware Multi-Agent Traceability describes a shared knowledge graph as a coordination surface with confidence calibration and conflict resolution.[25] A 2026 systematization of software-artifact traceability maps 22 artifact types and 23 associations, while noting that research is still disproportionately focused on code-related links.[16]

So the useful question is not, "Has anyone thought about persistent context or traceability before?"

They have.

The more precise question is:

How do we combine specification synthesis, project-wide impact analysis, provenance, human approval, and multi-agent access into a practical delivery loop that a team can operate?

That is the design space for GG-SDD. Its defensible contribution is not a new category; it is a deployable production synthesis: brownfield-first ingestion, a broader intent-to-operations thread, explicit provenance and freshness, vendor-neutral MCP access, and longitudinal evidence that the workflow improves real delivery.

A working definition

A project-owned traceability graph turns scattered artifacts into a governed change loop.

Graph-Grounded Specification-Driven Development is a traceability-first AI-DLC pattern in which each proposed change is derived from and written back to a versioned, project-scoped graph linking product intent, requirements, constraints, decisions, implementation, verification, and operational evidence.

The graph is not a giant prompt. It is not merely a vector store. It is not automatically the canonical source for every artifact.

It is a typed and permission-aware traceability layer that helps the system answer questions such as:

Which product goal motivates this request?
Which approved requirements and constraints govern it?
Which architectural decisions apply, and which have been superseded?
Which services, schemas, APIs, code modules, tests, and owners may be affected?
What evidence supports each claim in the proposed specification?
What changed during implementation, and was the specification reconciled afterward?

The project traceability graph

A useful graph schema should include more than code symbols.

Example node types include: ProductGoal, Requirement, Constraint, ArchitectureDecision, Component, Service, API, SchemaEntity, CodeModule, Test, Deployment, Incident, Metric, Owner, ChangeRequest, SpecificationVersion.

Example relationships include: motivates, refines, constrains, depends_on, implemented_by, verified_by, deployed_as, observed_by, owned_by, derived_from, supersedes, conflicts_with, changed_by.

The relationship model matters because many engineering questions are multi-hop questions.

"Where is authentication implemented?" is largely a repository retrieval problem.

"If we add team-level billing limits, which product commitments, authorization rules, database constraints, webhooks, tests, operational dashboards, and rollout decisions must change?" is a traceability and impact-analysis problem.

A graph does not eliminate semantic search. The strongest retrieval architecture is usually hybrid:

deterministic graph traversal for known relationships;
sparse retrieval for exact terms, identifiers, and domain language;
dense retrieval for semantic similarity;
ranking by freshness, approval status, and source authority;
optional agentic exploration when the relevant path is not yet known.

The goal is not to send the whole project to the model. It is to construct a minimal, evidence-rich impact packet for the decision being made.

Provenance is more important than volume

A context layer becomes dangerous if it is confidently stale.

Every material node and edge should carry operational metadata:

canonical source URI;
source owner;
version, hash, or timestamp;
validity interval and environment;
approval status;
confidentiality and access controls;
extraction confidence;
last verification time.

Generated relationships should not be indistinguishable from approved ones. An LLM may propose that a requirement is implemented by a particular service, but the link should retain its evidence and review state.

Similarly, old decisions should not simply disappear. An architecture decision record captures the context and consequences of a decision; when the decision changes, a new record should supersede the old one instead of rewriting history.[17]

This makes the graph a materialized view of project truth, not an opaque replacement for the systems that own that truth.

From a stakeholder request to a reviewed specification

Consider a stakeholder request:

Let workspace administrators set a monthly AI-usage budget and prevent overages.

An ordinary coding agent may search for "usage," find billing code, and begin editing.

A GG-SDD workflow should first produce an impact-aware specification.

A small request can have a large impact path. The graph makes that path inspectable before implementation.

1. Normalize the intent

The system separates the desired outcome from the proposed solution:

Who is allowed to set the budget?
Is the limit hard or soft?
Is usage measured in tokens, cost, requests, or a normalized credit?
What happens to in-flight work when the limit is reached?
Are enterprise contracts allowed to override the default behavior?

Only material ambiguity should trigger clarification. The objective is not to turn every request into a bureaucratic interview.

2. Retrieve an impact subgraph

The system follows relevant relationships across:

pricing and entitlement requirements;
authorization decisions;
usage accounting;
database tables and billing events;
background jobs;
customer notifications;
existing tests;
incident history;
deployment and rollback constraints.

3. Synthesize a source-grounded draft

The resulting specification should include:

clarified behavior and non-goals;
acceptance criteria;
affected components and owners;
data, API, and migration changes;
known conflicts with approved decisions;
implementation options and trade-offs;
test and observability requirements;
rollout and failure-recovery plan;
references to the evidence used.

4. Apply human approval gates

The system can prepare the evidence and detect inconsistencies, but responsibility remains human.

A product owner approves behavior. An engineer approves architecture. Security or operations approves high-risk changes. The approved snapshot becomes the contract passed to implementation.

5. Execute through any compatible client

MCP can expose approved specifications, impact packets, graph queries, and controlled actions to Codex, Kiro, or another agent host.

This is an interoperability layer, not the consistency engine itself. The MCP specification is explicit that it standardizes context exchange but does not dictate how an AI application manages the provided context.[18]

6. Reconcile after implementation

After the pull request, tests, and deployment, the system compares what was delivered with what was approved:

Did the implementation touch an unplanned service?
Were all acceptance criteria verified?
Did an implementation discovery change the design?
Was a new operational constraint learned?
Does an ADR need to be created or superseded?

Only verified changes should update approved project knowledge. This closes the loop that ordinary documentation workflows often leave open.

Why this helps multiple developers and agents

The value of GG-SDD is not that one agent becomes omniscient. It is that multiple participants have a shared semantic frame.

Without a shared context layer, each developer or agent reconstructs the project from local files, conversations, and memory. That produces repeated discovery, inconsistent assumptions, and incompatible plans.

With a traceability graph:

two agents can retrieve the same approved constraints;
a change to a shared API can identify parallel specifications that depend on it;
an architecture decision can propagate as a constraint without copying the full ADR into every prompt;
reviewers can inspect the evidence path instead of trusting an unattributed summary;
implementation discoveries can be written back once and reused by the team.

This does not eliminate coordination. It makes coordination observable.

Vercel and Supabase are implementation choices, not the methodology

Vercel and Supabase are convenient for this style of work because they provide strong programmable interfaces and MCP integrations. A development agent can inspect projects, deployments, logs, schemas, migrations, functions, and other operational state without relying entirely on manually prepared documents.

The architecture is cloud-neutral. AWS, Hetzner, self-hosted systems, or another stack can participate as long as the project has secure and inspectable interfaces.

The important design rule is least privilege.

Supabase explicitly warns against connecting its MCP server to production data, recommends development environments, project scoping, read-only mode, feature-group restrictions, branching, and manual approval of tool calls.[19] A GG-SDD implementation should treat these controls as part of the methodology, not as optional deployment details.

What can go wrong

A graph-backed workflow introduces its own failure modes.

The graph can be stale

If ingestion lags behind the canonical systems, retrieval can produce a coherent but outdated answer. Freshness service-level objectives, invalidation, visible timestamps, and reconciliation jobs are essential.

The graph can encode false relationships

An extracted link may be plausible without being true. Critical edges require source evidence, confidence, and an approval state. Automated extraction should propose knowledge, not silently canonize it.

Sources can disagree

The requirements, code, and deployment may describe three different realities. The system should surface the conflict and route it to an owner, not choose whichever source happens to rank highest.

Permissions can leak through retrieval

The context layer must preserve source-system access controls. A user who can query an MCP server should not automatically see every node in the graph.

Untrusted content can attack tools

Tickets, logs, or database records can contain prompt-injection content. Retrieval and action permissions must be separated, and state-changing tools should require explicit approval.

The process can become heavier than the problem

A small greenfield feature in one repository may not justify ontology management and multi-stage approval. GG-SDD needs a lightweight path. "Use the smallest process that preserves the required evidence" is a better principle than forcing every change through the same machinery.

How to test whether GG-SDD actually works

The framework should be evaluated against baselines, not marketed through anecdotes.

A useful study would take 30–50 historical feature requests of different complexity and run them through four modes:

Repository only: request plus ordinary repository tools.
Static SDD: request, steering files, and a structured specification workflow.
Semantic RAG: static SDD plus vector and sparse retrieval.
GG-SDD: typed traceability graph, provenance-aware retrieval, impact analysis, consistency validation, and approval gates.

The study should freeze the source snapshot, control the model family and token budget, repeat runs to measure variance, and use blinded senior-engineer review.

Relevant metrics include: time from request to approved specification; number of clarification rounds; affected-component precision and recall; source-grounding precision; conflict-detection precision and recall; percentage of specifications accepted without major revision; cross-run and cross-developer consistency; architecture-review rework; pull-request cycle time; escaped regressions; token and infrastructure cost; freshness and permission-policy violations.

Until these measurements exist, claims such as "higher consistency" or "less rework" should be presented as design goals and hypotheses, not established outcomes.

The real opportunity

The market does not need another promise that an agent can write an application from one prompt.

It needs a reliable way to preserve why a product works the way it does while humans and agents change it.

Specification-driven development makes intent explicit. Context engines improve retrieval. Requirements traceability connects artifacts. Knowledge graphs make relationships queryable. MCP makes those capabilities portable across agent clients.

GG-SDD proposes that these should be operated as one closed-loop system:

request → evidence → impact → specification → approval →
implementation → verification → reconciliation

That is the difference between generating code quickly and evolving a software product coherently.

The model will change. The IDE will change. The preferred agent will change. The durable asset is the project's traceable, governed understanding of itself.

If you are experimenting with spec-driven workflows, context engines, or traceability graphs on a real codebase, I would like to hear where they break for you — the comments are open.

References

Anthropic, "Effective context engineering for AI agents", September 29, 2025
GitHub Spec Kit, "Agentic SDD"
Kiro, "Specs" and structured planning workflow
GitHub Spec Kit, "Spec Persistence Models"
Sourcegraph, "Cody is generally available", December 14, 2023
Aider, "Repository map"
Augment, "The Context Engine"
Qodo, "Introducing Qodo's Context Engine", September 10, 2025
Factory, "The Command Center for Software Development"
Allstacks, "Engineering Context Graph"
OneTrace, "System of record for AI-built software"
Strata19, "An engine-driven platform for structured software development"
Cheng, Wu, and Hu, "Dataflow-Guided Retrieval Augmentation for Repository-Level Code Completion", ACL 2024
Liu et al., "CodexGraph: Bridging Large Language Models and Code Repositories via Code Graph Databases", NAACL 2025
Alturayeif, Ahmad, and Hassine, "TraceLLM", 2026
Chen et al., "SoK: Systematizing Software Artifacts Traceability", 2026
AWS Prescriptive Guidance, "Architectural decision record process"
Model Context Protocol, "Architecture overview"
Supabase, "MCP Server" and security guidance
AWS, "AI-Driven Development Life Cycle: Reimagining Software Engineering", July 31, 2025
AWS Samples, "AIDLC Collaborative", 2026
Tabnine, "Context-Driven Requirements Planning", 2026
SpecGraph, brownfield specification and MCP workflow
"TraceDev: A Traceability-Driven Multi-agent Framework for Requirement-to-Code Development", ISSTA 2026
"Trust-Aware Multi-Agent Traceability", 2026

Publication note: This article presents a proposed framework and implementation pattern based on practical development work. It is not a patent-landscape review or a claim that the underlying techniques are individually novel.

GEO for Healthcare: How Regulated Brands Can Win AI Visibility Without Risk

Oleksii Sytar — Fri, 01 May 2026 07:44:51 +0000

GEO for Healthcare: How Regulated Brands Can Win AI Visibility Without Risk

Originally published on OUTRANKgeo Blog

When someone asks ChatGPT "What is the best platform for managing patient intake?", or "Which telehealth company should I trust for chronic care?", the AI model answers. And whoever it names wins the click — without a single ad dollar spent.

For healthcare brands, this is both a massive opportunity and a minefield. The same AI systems that can drive qualified patient or customer leads also apply what Google calls E-E-A-T — Experience, Expertise, Authoritativeness, and Trustworthiness — with extra weight on the "Your Money or Your Life" (YMYL) content category.

This guide is for healthcare brands, health-tech companies, and medical SaaS products that want to appear in AI-generated answers without violating HIPAA guidelines, FDA advertising rules, or the trust expectations of their audience.

Why Healthcare Brands Get Extra Scrutiny from AI Systems

AI models like ChatGPT, Perplexity, and Gemini are trained on vast corpora that include published research, news, government sources, and user-generated content. For healthcare queries, these models apply conservative citation standards — because a wrong recommendation about medication or a misleading claim about a treatment outcome has real-world consequences.

Healthcare brands face two asymmetric risks in GEO:

Underrepresentation: Being absent from AI answers even when you're a legitimate, high-quality provider — because you haven't built the right signal ecosystem.
Misrepresentation: Being mentioned inaccurately by AI models that synthesize incomplete or outdated information about your product.

The playbook for healthcare GEO is not about gaming the system. It is about building the kind of authoritative, multi-source presence that AI models are explicitly trained to surface.

The Compliance-Safe GEO Framework for Healthcare Brands

1. Build Third-Party Credentialing Signals

AI models give disproportionate weight to what third parties say about your brand compared to what you say about yourself. For healthcare brands, the highest-signal sources are:

Peer-reviewed or trade publications: Being cited in NEJM, JAMA, Health Affairs, MedCity News, or Fierce Healthcare
Government and association sources: Listings in CMS databases, HIMSS directories, Joint Commission recognition pages, and NIH grant recipient lists
G2, Capterra, and Trustpilot for health-tech: Review platforms increasingly used by AI systems to surface product comparisons
Academic conference proceedings: Being mentioned in HIMSS, ViVE, or RSNA write-ups creates durable signal

2. Publish Condition-Agnostic Thought Leadership

The GEO-safe path is thought leadership that describes the category, the problem, and the market — without making claims about individual treatment outcomes.

Examples of compliant thought leadership that builds GEO signal:

"How AI is changing chronic disease management workflows" (category education)
"What health systems look for in a patient engagement platform" (buyer education)
"The state of remote patient monitoring: what the data says" (research synthesis)

3. Establish Named Expertise (the E-E-A-T Play)

When a named person at your company — a Chief Medical Officer, a clinical advisor, or a research lead — is cited in external sources, those citations associate that person's credibility with your brand in AI training data.

Have your CMO publish a bylined column in a trade publication
Submit expert quotes to healthcare journalists
Publish LinkedIn articles under your clinical experts' names
Participate in podcasts that get transcribed and indexed

4. Optimize FAQ and Schema for Health Queries

AI systems that perform live retrieval (like Perplexity) actively index your website. Compliant FAQ examples:

"How does [your platform] integrate with EHR systems?" (technical, non-clinical)
"What certifications does [your company] hold?" (credentialing, factual)
"How do health systems typically deploy [your solution]?" (implementation, not outcomes)

Add FAQ schema markup to these pages. AI systems specifically look for well-structured, factual Q&A content when synthesizing category answers.

5. Monitor What AI Systems Are Actually Saying About You

This is non-negotiable for regulated industries. AI models can hallucinate or repeat outdated information — including incorrect claims about your regulatory status, certifications, or product capabilities.

Regular AI visibility monitoring — asking ChatGPT, Perplexity, and Gemini category-specific questions and reviewing the answers — lets you catch misrepresentations early.

What NOT to Do: The Healthcare GEO Red Lines

Do not publish outcome claims as GEO content. "Our platform improves patient outcomes by X%" creates regulatory risk if AI models repeat the claim out of context.
Do not buy links from medical content farms. AI models trained to detect low-quality medical content will downweight brands associated with link farms.
Do not ignore negative AI mentions. The corrective action is publishing authoritative counter-content.
Do not let your AI visibility go unmonitored. What AI systems say today is what your next prospect may read tomorrow.

The 30-Day Healthcare GEO Plan

Week 1: Run baseline AI queries across ChatGPT, Perplexity, and Gemini — document what's accurate, missing, and wrong
Week 2: Audit your third-party presence on review platforms, trade directories, and association listings
Week 3: Publish two pieces of compliant thought leadership — one on your domain, one to a trade publication
Week 4: Establish a weekly AI monitoring routine and track changes

The healthcare brands that will dominate AI search in 2026 are the ones building this infrastructure now. GEO is still early enough that first movers in regulated categories get outsized advantage.

Want to see how your healthcare brand currently appears in ChatGPT, Perplexity, and Gemini? Run a free AI visibility scan at OUTRANKgeo — no credit card required.

GEO: What Generative Engine Optimization Means for Brand Visibility (2026 Guide)

Oleksii Sytar — Sat, 18 Apr 2026 11:32:48 +0000

The Google Era Had SEO. The AI Era Has GEO.

For 20 years, the playbook was clear: rank on page 1 of Google, get traffic.

Now something has shifted. A growing slice of searches never reach a list of results at all. The user asks ChatGPT or Claude a question, gets a synthesized answer, and stops. No click. No result page. Just: "here's the answer."

For brands, this creates a new question that nobody has a clean answer to yet: are you in that answer?

This is what Generative Engine Optimization (GEO) is starting to mean — the practice of understanding and improving how your brand appears in AI-generated responses.

Why it's different from SEO

Traditional SEO is a ranking problem. You're competing for positions 1–10 on a results page.

GEO is a presence problem. There's no position 2. Either the AI mentions your brand in its response or it doesn't.

The mechanics underneath are also different:

Google ranks based on links, authority, and keyword relevance
LLMs "rank" based on training data, context salience, and how confidently a brand is associated with a category
Building LLM presence requires different inputs: mentions in trusted sources, consistent category association, verifiable claims

What brands actually need to know

Three questions define GEO strategy right now:

1. Am I being mentioned?
The first step is basic visibility. For any given category query ("best [tool] for [use case]"), is your brand named? If not, everything else is secondary.

2. Where am I mentioned relative to competitors?
AI responses often implicitly rank brands by how prominently they're featured. "Brand A is excellent, Brand B is also worth considering" is a worse position than being Brand A.

3. What's being said about me?
Some AI mentions actively hurt. "Brand X exists but has limitations" is worse than not being mentioned at all. Quality of mention matters, not just presence.

The visibility gap is already real

The brands that show up in ChatGPT and Claude responses today are overwhelmingly the ones that were well-documented in training data — established players, heavily covered startups, products that got significant media attention pre-2024.

Newer brands, niche tools, and anything that launched in the past 12-18 months has a visibility gap. They exist. They may be excellent products. But the LLMs don't know them well enough to recommend them.

This gap is addressable. It just requires different tactics than traditional SEO.

What actually moves AI visibility

Based on what we've learned building OUTRANKgeo (a tool that tracks AI visibility across ChatGPT and Claude):

Trusted source mentions — LLMs heavily weight content from sites they consider authoritative. A mention on a well-indexed, trusted domain carries more weight than 10 mentions on thin content farms.

Category association density — The more consistently your brand appears alongside the right category terms in its source material, the more confident the LLM becomes in recommending you for those queries.

Verifiable, specific claims — Vague descriptions don't stick. Specific claims ("processes X in Y time," "used by Z type of customer") are more trainable and more quotable.

Cross-platform presence — A brand that appears in technical documentation, product reviews, forum discussions, and news coverage has better LLM recall than one with a polished website but no surrounding conversation.

How to measure where you stand

The challenge with GEO is that it's been largely untrackable. Traditional SEO tools don't show LLM presence. Google Search Console doesn't know what ChatGPT said.

Tools like OUTRANKgeo are starting to fill this gap — running automated queries across LLMs, scoring brand presence, and showing how you compare to competitors in AI-generated responses.

It's an early-stage discipline. The measurement frameworks are being built now. The brands investing in understanding their AI visibility today will have a significant head start when this becomes standard practice — which it will.

What to do about it

If you want to start improving your GEO:

Run a baseline scan — understand where you currently stand in AI responses for your key category queries. OUTRANKgeo offers this for free.
Get mentioned in trusted sources — prioritize earning coverage in publications that LLMs weight highly for your category. Quality over quantity.
Be specific about what you do — in all public content, be precise about your category, your users, and your differentiators. Vague positioning is invisible to LLMs.
Monitor regularly — AI visibility can shift as models update and new content enters training data. Set up tracking now so you have baseline data when it matters.

The brands that figure this out in the next 12 months will look very smart in 3 years.

OUTRANKgeo is a free tool for tracking AI brand visibility across ChatGPT and Claude. Run a scan at outrankgeo.com.

How we built OUTRANKgeo: an AI search visibility tracker built by AI agents

Oleksii Sytar — Wed, 15 Apr 2026 18:43:16 +0000

When I started WWG, I had a specific bet: could a software company be run almost entirely by AI agents?

Not "AI-assisted" — actually run by agents. CEO, CTO, CMO, engineers, QA, marketing. Each with a role, a task queue, and a heartbeat schedule. I'd check in once a day.

OUTRANKgeo is the first product that came out of this experiment. And the way it got built is at least as interesting as what it does.

What OUTRANKgeo does

OUTRANKgeo tracks your brand's visibility in AI-generated search responses — specifically ChatGPT and Claude.

The problem it solves: 60% of searches now end without a click. AI search makes this worse — there's no list of results, just one synthesized answer. Either your brand is in that answer or it isn't. We built OUTRANKgeo because we couldn't find a tool that told us where we stood.

Enter a brand or URL. The tool runs 5+ queries in your category across ChatGPT and Claude, scores your AI visibility, and shows which competitors appear where you don't.

Stack: Next.js (frontend), Supabase (database + auth), Railway (worker service), GCP, Vercel (deployment)

Free scan: https://outrankgeo.com — no credit card, results in minutes

How it was built: the AI-agent company architecture

The build team was 11 AI agents running on Paperclip — an agentic work management system. Each agent has:

A defined role (CEO, CTO, Code Reviewer, QA Engineer, Content Marketer, etc.)
A task inbox
A heartbeat schedule (wakes up, works, reports, sleeps)
A budget (Claude API costs)

Agents communicate via task comments. When the CTO is blocked, they create a task for the CEO. When code needs review, it gets routed to the Code Reviewer agent. No Slack. No meetings. No standups.

What actually worked

The agents shipped a functional product. That's the headline. Code was written, reviewed, tested, and deployed without human engineers. The CI/CD pipeline ran. Bugs were caught in QA.

The task management system (Paperclip) was the critical layer. Without structured task handoffs, agents would have lost context and duplicated work constantly. With it, they could operate across sessions with reasonable continuity.

GEO scan accuracy was validated by the QA agent running real test queries and comparing outputs. The Happy Path — sign up → add brand → run scan → see results — was verified before launch.

What didn't work (yet)

Agent memory is limited. Each heartbeat is a fresh context window. Agents sometimes repeat analysis they've already done. We're working on better memory layers.

Context loss between sessions. Complex decisions sometimes need to be reconstructed from task comments. Longer tasks require careful documentation or agents drift.

Confident wrongness. The worst failure mode: an agent making a definitive-sounding decision that's subtly incorrect. We added more in-review checkpoints to catch these.

The architecture decision I'd make differently

I'd build memory and context as a first-class system earlier. The agents work well on discrete tasks. They struggle with continuity across many sessions of a complex project. This is solvable — we just underinvested in it early.

Where this goes

OUTRANKgeo is the proof of concept. If an AI-agent team can ship a SaaS product that works and gets real users, the cost structure of software companies changes fundamentally. We're running that experiment live, in public.

Try the product: https://outrankgeo.com
Follow the build: updates coming to LinkedIn and here.

Questions welcome — happy to go deep on the agent architecture, the Paperclip system, or the GEO/AI visibility problem.