AI-native product engineering

AI-native.AI-flavored.

Product engineering for startups and enterprises that want to move faster — without growing engineering overhead.

Ask Logos AI — we'll point you to the right track
See how we work →·
Practitioners, not a bench/Model- & stack-agnostic/Built to transfer
What we build

Three doors in. Pick yours.

Same team, same standard. Where you come in depends on what you're carrying.

For product & eng teams

AI-native software engineering

You bring
An idea or backlog
We bring
A hands-on, AI-paired pod
Best fit
Founders and CTOs who need capacity, not a hire
Learn more
For teams automating workflows

Agentic AI systems

You bring
A workflow worth automating
We bring
Agents wired into your real systems
Best fit
Teams ready to move past a chatbot pilot
Learn more
For leadership teams

AI-native transformation

You bring
A team ready to change how it works
We bring
An audit, a roadmap, and delivery
Best fit
Enterprise leaders deciding where to start
Learn more
How we work

The stages look familiar.
What happens in them doesn't.

Spec01
Old way
PM doc, weeks of revisions
You and an engineer turn it into a real plan together.
Comes out as a scoped architecture and a risk list, not a slide deck.
Build02
Old way
Hand-typed, line by line
AI drafts, the engineer directs and decides.
Every AI-generated diff is read and owned by an engineer before it merges.
Review03
Old way
Queued for days, whoever's free
AI flags issues first, a human signs off.
Automated checks catch regressions and security gaps before a person even looks.
Ship04
Old way
Batched into sprints, bundled
Ships independently, one change at a time.
Feature-flagged, so a bad change gets switched off instead of a panicked rollback.
Practitioners, not a benchModel- and stack-agnosticBuilt to transfer
Proof, not promises

Engineering, not case studies.

Real work, real numbers — shipped across Edtech, Renewable energy, HealthTech, Fintech, Audtech. Ask about any project below for the details.

Edtech

AI doubt solver

A RAG engine over one of India's largest question banks — when a student's question isn't already covered, it generates and stores a new answer on the fly, so the bank keeps growing on its own. That's what took the cost of answering a question from ₹30 (done manually) down to ₹1, at close to a million questions a month.

₹30 → ₹1 cost per question
~1M questions answered / month
Python · LangChain · LangGraph · Pinecone · Neo4j · Gemini API
View case study
HealthTech

Health Companion

A multi-agent system built on Bodhi, the client's knowledge graph of medical concepts and terms, doing double duty. On the front end, agents handle prescription reading, smart report generation, lab report explanations, symptom checking, and appointment booking to bring in new patients. On the back end, the same system takes order-status and report-status checks off the support team's plate. It runs across WhatsApp, web, and the mobile app, engaging 25,000+ leads and patients a month at the lowest CAC of any lead-generation channel the client runs, while pre-empting 50+ support calls a day.

25,000+ leads & patients engaged / month
Lowest CAC of all lead-generation channels
50+/day support calls pre-empted
Python · LangChain · LangGraph · Bodhi knowledge graph · WhatsApp API
View case study
Renewable energy

CalcX

A sales workbench where a field rep — or the prospect themselves — can model capacity, financing, and battery setup over a 5-20 year horizon and get IRR back instantly.

₹500–700 Cr in annual sales supported
Python · FastAPI · React · MongoDB
View case study
Edtech

AI Mentor

A multi-agent personal mentor that handles onboarding, weekly planning, test analysis, and learning-path generation — wired directly into the client's own student data and content library.

100,000+ paying students, plus free users for lead capture
Python · LangChain · LangGraph · MCP · Neo4j · Gemini API
View case study
Edtech

Multi-channel lead nurturing engine

Agent-run outreach across phone and WhatsApp that reads each lead's score and disposition, then hands off cleanly between channels instead of restarting the conversation.

+60% conversion vs. spray-and-pray, over 3 months
Python · LangChain · LangGraph · LiveKit · OpenAI · Sarvam · SIP / WebRTC
View case study

Pick a path and talk it through — literally.

Not sure which door is yours? Start anywhere — we'll point you to the right one.

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