DEV Community: fatimamadiha0333-max

We built a Deal Intelligence Agent with Persistent Memory - it never forgets a sales objection

fatimamadiha0333-max — Tue, 29 Sep 2026 16:56:03 +0000

Sales deals take 3-6 months with 20+ calls and emails. Every rep I spoke to wastes 30 mins before a call re-reading scattered CRM notes, and still misses the key blocker.

Normal AI agents are stateless. They say "Acme Corp is a 200-employee SaaS company" but forget that last week their CTO said "you're 30% more expensive than CompetitorX".

So we built a Deal Intelligence Agent that REMEMBERS.

What it does:

Remembers: Objections, competitors, stakeholder concerns, pricing, sentiment across the full deal cycle
Briefs: Instant 30-sec briefing before any call
Suggests: Winning tactics learned from past closed-won deals

Stack: Python + Hindsight AI (for persistent memory) + OpenAI + Streamlit

Before vs After:

Before (Normal RAG): "Acme is a prospect. Last contact 5 days ago."

After (Our Agent): "Acme $28k Deal - Blocker 1: CTO thinks 30% expensive vs CompetitorX. Blocker 2: CFO worried about implementation. Last promise: Send ROI calculator. Winning tactic: Comparison Sheet + Quarterly payment closed 70% similar deals."

Why not just Vector DB?
Hindsight gives semantic memory - it knows "budget too high" = "pricing objection", persists for months, and learns patterns across deals.

Would love your feedback:

What memory features would be most useful?
How would you handle deal health scoring?

GitHub: https://github.com/fatimamadiha0333-max/deal-intelligence-agent
Full writeup: [ADD YOUR DEV.TO ARTICLE LINK HERE]

buildinpublic #AI_Agents #SalesTech

Building a Deal Intelligence Agent with Persistent Memory

fatimamadiha0333-max — Tue, 29 Sep 2026 16:38:27 +0000

Sales deals are 3-6 months long with 20+ touchpoints. Reps waste hours re-reading scattered CRM notes before calls and still miss key objections like pricing concerns or competitor mentions.

Problem we're solving

A typical deal has calls, emails, Slack, CRM fields. Information is everywhere. Reps spend 30 minutes before every call re-reading notes.

Why normal AI agents forget

Traditional RAG agents are stateless. They treat every query as new. They give generic info like "Acme is a 200-employee SaaS company" but miss that last week the CTO said "your pricing is 30% higher than Competitor X".

Our solution

Deal Intelligence Agent that remembers every interaction across a deal cycle - objections, competitors mentioned, stakeholder concerns, and pricing discussions. Over time, it learns which objection-handling approaches work best.

How persistent memory works

We use Hindsight AI as the memory core. Not just vector search - it's episodic memory that stores deal_id, objection type, sentiment, and evolves over time.

System architecture

User Call / Email -> Transcription -> Hindsight Memory Extraction -> Deal Memory Graph -> Briefing Agent

Technologies used

Python, Hindsight AI, OpenAI API, Streamlit

How the agent remembers clients/deals

from hindsight import Hindsight
hindsight = Hindsight()

hindsight.store(
content="Acme Corp call on 2026-09-20",
metadata={
"deal_id": "acme_corp_q4",
"objection": "pricing too high vs CompetitorX",
"stakeholder": "CTO - John Miller",
"competitor": "CompetitorX",
"next_step": "send ROI calculator"
}
)

query = "What are the key blockers for Acme Corp?"
deal_context = hindsight.recall(query=query, filter={"deal_id": "acme_corp_q4"}, top_k=5)

Example workflow

BEFORE Hindsight:
"Acme Corp is a prospect. Last contact was 5 days ago."

AFTER Hindsight:
"Briefing for Acme Corp ($28k Deal):
Blocker 1: CTO thinks 30% expensive vs CompetitorX
Blocker 2: CFO worried about implementation
Last Promise: Send ROI calculator
Winning Tactic: Comparison Sheet + Quarterly Payment closed 70% similar deals"

Challenges & solutions

Challenge: Vector DB couldn't link "budget too high" and "pricing concern" as same objection. Solution: Hindsight's semantic memory understands intent.

Demo / Screenshots

[Add your frontend screenshot here]
[Add your briefing output screenshot here]

Future improvements

Gong / Chorus call auto-integration
Deal Health Score based on memory sentiment
Auto Email Drafting

GitHub / Demo links

Code: https://github.com/fatimamadiha0333-max/deal-intelligence-agent
Memory: github.com/hindsight-ai/hindsight

We built a Deal Intelligence Agent with Persistent Memory - it never forgets a sales objection

fatimamadiha0333-max — Tue, 29 Sep 2026 16:37:36 +0000

Sales deals take 3-6 months with 20+ calls and emails. Every rep I spoke to wastes 30 mins before a call re-reading scattered CRM notes, and still misses the key blocker.

Normal AI agents are stateless. They say "Acme Corp is a 200-employee SaaS company" but forget that last week their CTO said "you're 30% more expensive than CompetitorX".

So we built a Deal Intelligence Agent that REMEMBERS.

What it does:

Remembers: Objections, competitors, stakeholder concerns, pricing, sentiment across the full deal cycle
Briefs: Instant 30-sec briefing before any call
Suggests: Winning tactics learned from past closed-won deals

Stack: Python + Hindsight AI (for persistent memory) + OpenAI + Streamlit

Before vs After:

Before (Normal RAG): "Acme is a prospect. Last contact 5 days ago."

Why not just Vector DB?
Hindsight gives semantic memory - it knows "budget too high" = "pricing objection", persists for months, and learns patterns across deals.

Would love your feedback:

What memory features would be most useful?
How would you handle deal health scoring?

GitHub: https://github.com/fatimamadiha0333-max/deal-intelligence-agent
Full writeup: [ADD YOUR DEV.TO ARTICLE LINK HERE]

buildinpublic #AI_Agents #SalesTech

#

fatimamadiha0333-max — Tue, 29 Sep 2026 11:08:48 +0000

For a Reddit post, you can publish it in relevant subreddits such as:
r/artificial
r/LocalLLaMA
r/AI_Agents
r/MachineLearning (check rules first)
r/SideProject
r/startups
Your Reddit post should be shorter than the full article and should introduce your project.
Example:
Title:
We built a Deal Intelligence Agent with Persistent Memory for a Hackathon
Post:
Our team built an AI-powered Deal Intelligence Agent that remembers customer interactions, objections, stakeholder preferences, pricing discussions, and previous conversations across the entire sales cycle.
Unlike traditional AI chatbots that forget context, our agent uses persistent memory to maintain long-term relationships and provide better insights.
Tech Stack: Python, FastAPI, LLM APIs, Vector Database, Memory System.
We'd love feedback from the community:
What memory features would be most useful?
How would you improve this system?
GitHub: [Your GitHub Link] Demo: [Your Demo Link] Article: [Your Article Link]
Before posting on Reddit, always read the subreddit rules because some communities restrict self-promotion.
For your hackathon, a good combination is:
Technical Article: Medium / Dev.to
Professional Post: LinkedIn
Community Discussion: Reddit
This gives you wider visibility and stronger content submission evidence.

#building and deal intelligence agent with president memory

fatimamadiha0333-max — Tue, 29 Sep 2026 11:04:21 +0000

Problem we're solving

Sales deals are 3-6 months long with 20+ touchpoints. Reps waste hours re-reading scattered CRM notes before calls and still forget key objections like pricing concerns or competitor mentions.

Why normal AI agents forget

Traditional RAG agents are stateless. They treat every query as new. They give generic info like "Acme is a 200-employee SaaS company" but miss that last week the CTO said "you're 30% more expensive than CompetitorX".

Our solution

Deal Intelligence Agent that remembers every interaction across a deal cycle - objections raised, competitors mentioned, stakeholder concerns, pricing discussions. Over time, it learns which objection-handling approaches work best.

How persistent memory works

We use Hindsight AI as the memory core. Not just vector search - it's episodic memory that stores deal_id, objection type, sentiment, and evolves over time.

System architecture

User Call/Email -> Transcription -> Hindsight.store() with metadata -> Deal Memory Graph -> Hindsight.recall() -> Briefing + Tactic Suggestion

Technologies used

Python, Hindsight AI, OpenAI API, Streamlit for frontend

How the agent remembers clients/deals

[Paste the python code I gave you for store_interaction and get_briefing]

Example workflow

Before: "Brief me on Acme" -> Generic company info
After: "Brief me on Acme" -> "CTO pricing objection vs CompetitorX, CFO worried about implementation, you promised ROI calculator, Winning tactic: Comparison sheet + quarterly payment closed 70% similar deals"

Challenges & solutions

Challenge: Vector DB couldn't link "budget too high" and "pricing concern" as same objection. Solution: Hindsight's semantic memory.

Demo/screenshots

[Add your frontend screenshot + briefing output screenshot here]

Future improvements

Gong integration, Deal health score, Auto email drafting

GitHub/demo links

Code: https://github.com/fatimamadiha0333-max/deal-intelligence-agent
Memory: github.com/hindsight-ai/hindsight

HACKATON PROJECT

fatimamadiha0333-max — Tue, 29 Sep 2026 10:59:21 +0000

the article is part of the content submission, and you need a publicly accessible article link.
Where should you publish it?
The hackathon guide you shared listed these options:
Medium — easiest for a polished technical article
Dev.to — great for developer/hackathon content
Hashnode — good for technical/project write-ups
Substack — good for longer articles
LinkedIn Articles — useful if you want it attached to your professional profile
What I recommend for your team
Use Dev.to or Medium.
Your article can be structured like:
Title:
Building a Deal Intelligence Agent with Persistent Memory
Sections:
Problem we're solving
Why normal AI agents forget
Our solution
How persistent memory works
System architecture
Technologies used
How the agent remembers clients/deals
Example workflow
Challenges & solutions
Demo/screenshots
Future improvements
GitHub/demo links
Then publish it publicly and copy the article URL into the hackathon submission.