Gyan Niketan School
A full school website with a live events engine, push notifications and a 24/7 AI assistant, live in production at gyanniketan.in.
AI development
AI development that makes it to production.
We design and build custom AI products — LLM apps, agents and retrieval systems — that run reliably, cheaply and at scale. Not demos. Software your customers use every day.
From demo to production
The prototype worked. Production didn’t. We close that gap — retrieval, evaluation and cost control designed in from the first commit.
01
Understand
A free scoping call, then a written plan — the problem, the data, the model options, what success looks like and a fixed price.
02
Architect
We choose models, design the retrieval and tool layer, and write the first evaluation set before any feature code.
03
Build
Weekly demos on real data. You see the product get better every week and can steer it.
Problems we solve
Sound familiar?
The four things we hear most before a project starts — and what we do about each.
The prototype worked. Production didn't.
A notebook demo and a product are different things. We add the evaluation, guardrails, caching and monitoring that stop an LLM feature from failing quietly in front of users.
Answers are confidently wrong.
Hallucinations usually mean the model can't see the right data. We build retrieval (RAG) over your documents and databases so answers are grounded and cite their sources.
The API bill keeps climbing.
We route each request to the smallest model that can handle it, cache aggressively and trim prompts, so cost grows slower than usage.
Nobody on the team has shipped AI before.
We work inside your repo and leave behind code, docs and evals your team can own — no black boxes.
What’s included
Everything it takes to ship.
Every project starts with a free scoping call and a written build plan. You get a fixed-price quote for the whole build before any work starts — no hourly billing and no surprises.
- Discovery and a written build plan with a fixed-price quote
- Model selection across Gemini, OpenAI, Claude and open-weight models
- Retrieval-augmented generation over your documents and data (pgvector)
- Agents and tool use — AI that can search, fill forms and call your APIs
- Evaluation suites that catch regressions before your users do
- Guardrails, rate limits, cost controls and usage dashboards
- Web or in-app UI for the feature, built by the same team
- Deployment, monitoring and a hand-over your team can run
What we mean by “AI development”
Most businesses don’t need a new foundation model. They need an existing model to do one specific job well — answer questions about their policies, draft a report from their data, triage support tickets, or help a customer find the right product. That job has to happen reliably, in the right language, at a cost that makes sense.
That is the work we do. We take a real business problem, connect the right model to the right data, and wrap it in software people can actually use: a web app, a chat interface, an API or an automation that runs in the background.
AI-native from the first commit
AI isn’t a feature we bolt on at the end. It shapes the architecture from day one — how data is stored and indexed, how requests are routed between models, how every answer is logged and evaluated. Designing for it early is what separates an AI product that improves over time from one that quietly gets worse.
In practice that means:
- Retrieval before generation. If the answer lives in your documents, the model should read your documents. We build RAG pipelines with pgvector so responses are grounded in your data and can cite where they came from.
- Evaluation as a first-class feature. We write test sets of real questions and expected behaviour before we write prompts. Every change to a prompt or model runs against them, so we know whether it helped.
- Cost designed in, not optimised later. Model routing, caching and prompt compaction are part of the first version, not a panic after the first invoice.
Built by people who ship their own AI products
We are not only a services studio. We build and run our own AI products — Synka AI, Krishi AI, Gyanis AI and SearchMind. The patterns we use for clients are the ones we trust with our own users, and the mistakes we’ve already made are ones you won’t pay for.
For Gyan Niketan, one of Patna’s established CBSE schools, we built Gyanis AI — a campus assistant that answers questions from parents and students around the clock, alongside the school’s full website.
Who this is for
- Founders turning an AI idea into a first product that real users can try.
- Businesses that want AI working on their own data — documents, catalogues, tickets, records.
- Teams with an AI prototype that needs to become reliable, observable and affordable.
If you’re weighing up approaches, our guide to RAG vs fine-tuning for business apps explains the trade-offs in plain language.
Process
How we’ll work together.
A working demo every week, and a fixed price agreed before we start.
Understand
A free scoping call, then a written plan — the problem, the data, the model options, what success looks like and a fixed price.
Architect
We choose models, design the retrieval and tool layer, and write the first evaluation set before any feature code.
Build
Weekly demos on real data. You see the product get better every week and can steer it.
Launch & grow
We ship, watch the metrics and keep iterating — we don't disappear after launch.
Tech stack
Tools we trust in production.
We pick per project — these are the ones we reach for most.
- Gemini
- OpenAI
- Claude
- LangChain
- pgvector
- Supabase
- Python
- Node.js
- Next.js
- Cloudflare
Proof
Where we’ve done this.
How much does it cost to build an AI product?
It depends on scope — the number of features, where your data lives and how much traffic you expect. After a free scoping call we send a written plan with a fixed-price quote, so you know the full cost before we start.
How long does an AI project take?
Most builds go live in 3–8 weeks. A focused MVP can ship in under a month; a larger AI platform takes longer. You see a working demo every week.
Which AI model will you use?
Whichever fits the job. We regularly work with Gemini, OpenAI and Claude models, and with open-weight models when data must stay on your own servers. We pick per task, and often mix models to keep cost down.
Who owns the code and the IP?
You do — 100%. The code lives in your repositories from day one, and every engagement is invoiced with GST.
Will you sign an NDA?
Yes. We are happy to sign your NDA before the scoping call.
Do you support the product after launch?
Yes. We monitor, fix and keep improving what we build. Support is scoped in the plan so you know what's covered.
Let’s talk about your ai development project.
Tell us what you want to build. We reply within 24 hours — the scoping call is free.
Let’s build it