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AI Prototyping Services: Validate a GenAI Idea in Weeks, Not Quarters
As an AI prototype development company, we help you find out whether an AI idea works on your real data before you commit to building it. A typical validation prototype takes three to four weeks and costs USD 12,000–30,000 with our India-based team. You get a working prototype, an evaluation set with measured results, an architecture note and a clear go or no-go recommendation. This page explains what a prototype is and is not, what you receive, how the sprint runs, and what happens after it.
What an AI Prototype Is, and What It Is Not
Most disappointment with AI prototypes comes from mismatched expectations. Clients expect a near-finished product, while vendors deliver a demo that only works on hand-picked examples. A good prototype sits between those extremes: it proves or disproves a specific idea on real data, with measured results. The table makes the boundaries explicit, so both sides agree what the prototype should prove before any work begins. Agree these boundaries in the scoping call. Our prototypes are delivered by the same team behind our offshore development center in india.
| An AI prototype is | An AI prototype is not |
|---|---|
| A working test of one specific use case on your real data | A finished, production-ready product |
| Measured against agreed success criteria | Judged only by impressive demos |
| Built with real users for feedback | Tested only by the team that built it |
| Designed so the useful parts can be reused | A throwaway script with no structure |
| A basis for a clear go or no-go decision | A commitment to build regardless of results |
A Test of One Idea
A prototype answers one question, such as whether a model can reliably answer support questions from your documentation. Keeping scope narrow makes the answer clear and the timeline short. Broader ideas become later phases.
Measured, Not Demonstrated
Success criteria, such as answer accuracy on a test set or time saved per task, are agreed before the build. Results are reported against them, so the decision rests on evidence.
Built for Reuse
Although it is not production software, the prototype is structured so that retrieval pipelines, prompts and evaluation sets can be reused in a later build rather than discarded. Good structure saves weeks later.
Not a Finished Product
Authentication, full integrations, scaling, security hardening and polished interfaces belong to the production phase. Including them in a prototype slows it down and blurs its purpose. They are planned in the architecture note instead, ready for the next phase.
Scope and Deliverables
A prototype engagement has a fixed scope and a fixed set of deliverables, agreed in a short scoping call before work begins. The list below is what you receive at the end of a typical three-to-four-week validation prototype. Every deliverable belongs to you, whether or not you continue with us, so the work keeps its value even if the answer is no. Our AI prototype sprint packages this scope at a fixed fee.
| Deliverable | What it contains |
|---|---|
| Working prototype | A simple interface connected to your real data and a chosen model |
| Evaluation set | 50–150 real questions or tasks with expected outputs and scores |
| Findings report | Results against success criteria, failure analysis and recommendation |
| Architecture note | How a production version would be built, with risks and options |
| Cost estimate | Indicative cost per request and cost to reach production |
| Source code and prompts | Everything in your repository, documented |
Working Prototype
The prototype connects to your real documents or data and a chosen model, with a simple interface that target users can try. It is functional enough for honest feedback, not just a recorded demo.
Evaluation Set and Results
The evaluation set is the most valuable deliverable. It lets you compare models, prompts and vendors long after the sprint, and becomes the quality gate for any production build. You keep it permanently.
Findings and Recommendation
The findings report sets out what worked, what failed and why, with a clear recommendation: proceed to production, iterate further or stop. Each option includes its reasoning. Stakeholders get a plain-language summary.
Production Architecture and Cost
The architecture note and cost estimate show what a production version would need and roughly what it would cost to build and run, so the next decision is properly informed.
The Prototype Sprint, Week by Week
A validation prototype runs as a short, structured sprint with clear checkpoints. The structure keeps the work focused on the agreed question and makes progress visible each week. Most prototypes follow the plan below, adjusted for data availability and scope. Your involvement is heaviest at the start, when success criteria and data are agreed, and at the end, when results are reviewed and decisions are made. Weekly check-ins keep everyone aligned.
| Week | Focus | Output |
|---|---|---|
| Week 1 | Scoping, success criteria, data access, evaluation set draft | Agreed criteria and first test set |
| Week 2 | Retrieval and model experiments | Baseline results against the test set |
| Week 3 | Iteration and simple interface | Improved results and a usable prototype |
| Week 4 | User testing, findings and recommendation | Final report, architecture note and handover |
Week 1: Criteria and Data
The team agrees success criteria with you, secures access to representative data and drafts the evaluation set with your domain experts. Clear criteria at this stage make every later decision easier.
Week 2: Baseline Experiments
Engineers build the first retrieval and model pipeline and measure it against the evaluation set. Baseline results show where the real difficulties lie, often in data rather than the model.
Week 3: Iteration and Interface
The team improves the weakest parts, such as retrieval or prompting, and adds a simple interface so target users can try the prototype on real tasks. Each change is measured against the evaluation set before it is kept.
Week 4: Testing and Decision
Target users test the prototype, results are measured against criteria, and the team delivers its findings, architecture note and recommendation in a review session with your stakeholders. Decisions are made with evidence.
The Path From Prototype to Production
A successful prototype is a starting point, not a finished feature. Taking it to production adds authentication, integrations, scaling, monitoring, security and continued evaluation, which typically costs two to four times the prototype fee. Planning that path before the prototype starts avoids surprises later. The stages below describe how a validated prototype becomes a dependable feature, and how our AI development services support each stage. The architecture note maps the route.
Pilot
A pilot puts the feature in front of a real team for several weeks, with authentication, logging and basic integrations. It tests reliability and value under everyday conditions before a wider rollout.
Production Build
The production build adds full integrations, security hardening, scaling, monitoring, guardrails and cost controls. The prototype's evaluation set becomes the release gate for every change. Releases follow agreed quality gates.
Ongoing Evaluation
After launch, the evaluation set grows with real failure cases, and quality, latency and cost are tracked continuously so the feature stays reliable as models and data change. Drift is caught early.
Team for Production
Production work usually needs a small dedicated team. Our dedicated developers model and pricing packages show options and costs. The engineers who built the prototype can often continue, keeping their knowledge in the team.
Handover and Code Ownership
A prototype should leave you with assets you fully own and can use with any team, not a dependency on the vendor that built it. Everything we produce during a prototype is assigned to you and handed over in a form another team could pick up. The sections below describe exactly what is handed over, how it is documented and the commitments that protect your ownership, as set out in our delivery guarantees.
Code and Prompts in Your Repository
All code, prompts, configuration and evaluation data live in your repository from the first day, not on our servers. Nothing needs to be transferred at the end. You can review progress at any time.
No Proprietary Lock-In
We do not build prototypes on proprietary internal platforms that you would need to licence later. Any reused components are handed over with full rights. Any future team can continue the work without licences or permissions from us.
Your Data Stays Yours
Data stays in your accounts, and model providers are configured not to train on it. Access is removed when the sprint ends, unless you continue. Confirmation is given in writing.
A Clear Handover Session
The final review includes a walkthrough of the code, evaluation set and architecture note, recorded so that any future team can understand the work. Questions are answered in writing after the session, too.
If the Answer Is No
A clear “no” is a valid result. If the prototype shows the idea will not work, the findings save you a far larger investment. A discovery sprint can explore alternatives.
Frequently Asked Questions
It builds a working test of a specific AI idea on your real data, measures it against agreed success criteria and recommends whether to proceed. A typical engagement delivers a prototype, an evaluation set, a findings report, an architecture note, a cost estimate and all source code.
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