Scope Definition Workshop
We cut the POC down to what actually tests the AI hypothesis.
AI Consulting & Governance
AI Proof of ConceptA Working Test, Not a Slide Deck.
AI proof of concept for the test that decides whether the AI is worth the product — a working POC in 4 to 6 weeks, with the data, the model, the eval and the user-facing slice, not a slide deck. The output is a tested answer, not a wishlist.
10+Years Experience
100+Projects Delivered
50+Expert Developers
20+Industries Served
Scope Definition Workshop
Core AI Workflow
Data Pipeline & RAG
Model & Prompt
User-Facing Slice
Eval & Recommendation
Overview
An AI POC is a test, not a small version of the product
An AI POC exists to answer one question: does the AI do the job well enough to justify the product? Everything that does not help answer that question is scope you are paying for twice. The POC is the test, the data is the proof, the eval is the answer.
We start by writing down the question your POC is testing, then cut the scope until only the parts that test it remain. Typically that is one core AI workflow, the data, the model, the eval, the user-facing slice and the cost. The POC is a working slice, not a slide deck.
This is the wrong engagement if the AI is already proven and you need a product. The right answer there is an AI MVP, not an AI POC.
- Fixed Scope — A written POC scope before we start, and a change process if it moves.
- You Own It — Repositories, models and cloud accounts in your name from the first commit.
- Tested Answer — A tested answer — the data, the eval, the cost — not a slide deck.
- Go / No-Go — A clear go / no-go recommendation, with the cost of the next step.
What we deliver
Everything included in our ai proof of concept
Core AI Workflow
The one AI workflow the POC lives or dies on, built properly.
Data Pipeline & RAG
The ingestion, the chunking, the embeddings and the retrieval the POC needs.
Model & Prompt
The model selection, the prompt and the context engineering, with the eval.
User-Facing Slice
The user-facing slice the test users can actually use, with the analytics.
Eval & Recommendation
The eval suite, the cost analysis and the go / no-go recommendation.
Our process
A proven process for successful delivery
01
Discover
We write down the question the POC must answer.
02
Plan & Design
We cut scope and design the core AI workflow.
03
Develop
We build in two-week sprints with a working slice every Friday.
04
Deploy
We ship the POC to a staging environment with the eval and the analytics live.
05
Optimize & Grow
We read the eval with you and recommend the next step.
Technology
Built with a stack that stays maintainable
Models
- OpenAI
- Anthropic
- Google Gemini
- Open source
Backend
- Python
- Node.js
- LangChain
- LlamaIndex
Data & Retrieval
- Pinecone
- Weaviate
- pgvector
- PostgreSQL
Frontend
- Next.js
- React
- TypeScript
- Tailwind CSS
What you can expect
- Typical POC Window
- 4-6 wksTypical POC Window
- Core AI Workflow Tested
- 1Core AI Workflow Tested
- Recommendation
- Go / No-GoRecommendation
- Code, Models and Accounts Owned by You
- 100%Code, Models and Accounts Owned by You
Industries we serve
Delivered for teams in your sector
Most POCs we build land between ₹4 lakh and ₹12 lakh, depending on the data pipeline, the model and the eval suite. After a 30-minute discovery call we send a written scope with a fixed price. If your idea needs more than that, we will say so before you commit.
Yes, through a change process rather than silently. Anything that replaces something already in scope is a straight swap. Anything additive gets a written estimate and a revised date before we start it. The change process is the single biggest reason POCs slip, so we handle it explicitly.
A go / no-go recommendation, with the data, the eval, the cost and the next step. The recommendation is the output of the POC, and the team uses it to decide whether to proceed to an AI MVP or to a different use case. The POC is a test, and the recommendation is the answer.
Not immediately. Many clients stay on a small retainer while they make the go / no-go decision, then move to an AI MVP with the POC as the foundation. We hand over documentation and the data, and the next step is a separate engagement.
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Ready to start your ai proof of concept project?Let’s scope it together.
Tell us the outcome you need. We’ll come back with an approach, a timeline and a written estimate.