Custom AI development — assistants and automation that behave.
AI that gives the same answer twice, cites its source, and does not invent numbers — built for real work, not demos.
Most AI pilots die for one reason — impressive in a demo, untrustworthy in production.
A chatbot that makes up a policy, a scoring tool that gives a different result each run, an OCR that quietly drops a digit — in a real business these are not quirks, they are liabilities. The hard part of AI is not calling a model. It is making the output reliable, grounded and auditable.
Uminber Designs builds custom AI systems for Indian businesses and government bodies, and we build them to behave. Where a decision must be defensible, we use deterministic scoring with hard gates instead of leaving it to a model's mood. Where an assistant answers questions, we ground it in your documents so it cites rather than invents. This is production AI — the kind you can put in front of a citizen, an evaluator or a paying customer.
Grounded, gated, defensible.
The value is in the grounding, the guardrails and the workflow we build around the model — not a thin wrapper over a public chatbot.
AI chatbots & assistants
Assistants that actually help — answering from your real content, handing off to a human when unsure, and staying inside their brief. They embed cleanly into your site or product, often as a single script. The Nix screening assistant for Gujarat's SSIP programme runs this way on the i-Hub portal.
RAG & knowledge systems
Retrieval-augmented generation lets an assistant answer from your own documents instead of guessing. We build the full pipeline — chunking, embeddings, a vector store and grounded prompts — so answers come with a source and stay current. AI that knows your policies, not the internet's.
Document AI & OCR
Piles of forms, invoices and scanned records are where staff hours quietly disappear. We build document AI that reads, extracts and structures this data — including handwriting and regional documents — with validation so a wrong read is caught, not filed. We proposed exactly this for Palanpur Nagarpalika.
AI automation & workflows
Not every task needs a chat window; many just need a step to happen on its own. We automate the repetitive middle — triage, classification, summarisation, routing — wiring AI into your existing workflow. We are honest about where a rule beats a model, and use the cheaper one.
Deterministic scoring & screening
When AI helps make a decision, that decision has to be consistent and explainable. We build deterministic scoring — fixed 0-to-100 logic with hard gates — so the same input always gives the same result. We built this for the SSIP/Shreya healthcare screening assistant and i-Hub Gujarat.
LLM integration
If you already have a product, we add AI into it without rebuilding — safely, with rate limits, cost controls and fallbacks. We integrate across major model providers and hosting, add observability so you can see what the AI did, and keep sensitive data handled correctly. A feature, not a science project.
Agentic AI & multi-agent systems
The jump from an assistant that answers to an agent that acts. We build agents that plan and complete multi-step work on their own — reading your systems, calling tools, drafting the reply, updating the record — and hand off to a person at the right moment. Scoped tightly, logged fully, and gated so they never step outside their brief. This is where 2026 demand is heading, and where most wrappers fall over.
Conversational & voice agents
Natural-language interfaces that replace the form and the dashboard. Ask a question in Hindi, Gujarati or English — an order status, a sales number, a policy — and get an answer, spoken or on screen, in seconds. Grounded in your data, role-aware, and built for real WhatsApp and phone-line latency, not a lab demo.
Computer vision & visual AI
Turning a camera into a sensor. Quality inspection on a line, count-and-condition from a photo, ID and document capture, footfall and safety monitoring — vision models tuned on your own images, with a human check wherever a missed read is expensive. Pairs directly with the IoT and ERP we build.
The demo era is over. Production is the product.
2026 moved AI from pilots to systems people depend on. That shift changes what a build has to be — and it is exactly the part we are hired for.
From assistants to agents
The market has moved past chatbots that only reply. The demand now is for agents that plan and finish multi-step work on their own — and the hard, valuable part is scoping and gating them so they act safely inside your systems.
RAG is the baseline, not the feature
Grounding a model in your own data is no longer clever — it is table stakes. We treat retrieval, citations and freshness as the minimum an assistant must do before it is allowed anywhere near a customer or a citizen.
Integration is the real moat
Anyone can call a model. Value comes from wiring AI deep into your ERP, CRM and workflow — which is why we build the software around the AI, not just the AI, so it survives contact with a real Monday.
Governance from day one
Access control, audit logs, hallucination limits and hard cost caps are part of the build, not a later add-on — because production AI has to be defended to an auditor, not just shown in a deck.
What you get is a system you can put your name behind.
We do not hand over a clever prompt and wish you luck. You get the whole pipeline — grounded on your own content, wrapped in guardrails, and wired into software your team already uses. The model is one part; the reliability around it is the work.
Every build is instrumented so you can see what the AI did, correct it, and prove it later — because AI that touches a citizen, an evaluator or a paying customer has to be defensible, not just impressive.
- A RAG pipeline — chunking, embeddings, vector store — that answers from your documents with a source
- Deterministic 0-to-100 scoring with hard gates where a decision must repeat exactly
- Document AI & OCR that reads forms, invoices and regional-language records, with validation on every field
- Observability, rate limits and cost controls so nothing runs away quietly
- An embeddable widget or an in-app feature — not a demo sitting beside your product
The point is a quieter operation.
Not a shinier demo — fewer wrong answers in front of people who matter, and hours the model handles so your team does not.
Fail safe, not silently.
We separate what genuinely needs a model from what a simple rule solves cheaper — and say so plainly.
The problem
Separate what genuinely needs a model from what a simple rule solves cheaper — and say so plainly.
The data
Assemble and clean the documents or data the system relies on, because AI is only as good as what it reads.
Build safe
Retrieval, validation, deterministic logic and fallbacks, so the system fails safe, not silently.
Test it
We test against real inputs and edge cases, measuring accuracy and consistency before anyone trusts it.
Deploy & tune
Ship with monitoring and cost controls, then tune as real usage teaches us where it strays.
Why teams trust us with the part that has to be right.
Most AI shops chase the demo. We are hired for the opposite — the version that survives a real evaluator, a real auditor and a real Monday. That comes from doing less magic and more engineering: fixed logic where a decision must hold, grounding where an answer must be true, and honesty about where AI simply is not the tool.
Because we also build the web, product and ERP around it, the AI lands inside working software with auth, data and dashboards — not as an isolated pilot that stalls the moment it meets your real workflow.
Deterministic where it counts
For decisions and scoring we use fixed, explainable logic — same input, same output. No black-box moods in front of a citizen or evaluator.
Grounded, not guessing
Our assistants answer from your documents and cite sources, so you get fewer confident wrong answers.
We build the whole system
Because we also do web, product and ERP, the AI ships inside working software with auth, data and dashboards — not as an isolated demo.
Honest about AI
We tell you when a rule beats a model and when AI is not the answer. That saves you money and disappointment.
AI you can put in front of a citizen, an evaluator or a customer.
Grounded, gated and logged — built to be defended, not just demoed.
AI that stands up to audited use.
i-Hub Gujarat
An incubation management system with a deterministic AI screening assistant that scores ideas consistently and explainably.
SSIP / Shreya
A healthcare idea-screening AI with deterministic 0-to-100 scoring and hard gates, for Gujarat's SSIP 2.0 programme.
Palanpur Nagarpalika
An AI and OCR pitch to digitise municipal records — reading, extracting and validating regional-language documents.
AI inside working software.
The assistant ships in the app or the ERP we build — not as an isolated demo beside it.
Embed it ↗
Put the assistant or automation inside the app or portal we build for you.
Work the data ↗
Put document AI and automation to work on the data already inside your ERP.
Predict from telemetry ↗
Turn sensor data into predictions — utilisation, anomalies and maintenance alerts.
Things you might be wondering.
What is RAG and why does my chatbot need it?
How do you stop AI from giving wrong or made-up answers?
Can AI read our invoices, forms and scanned documents?
Do you build custom AI or just plug in ChatGPT?
Is our data safe when using AI?
What is an AI agent, and do we actually need one?
Can the AI talk — voice or WhatsApp, in Indian languages?
How fast can we see a working AI system?
A process AI could take off your team's plate?
Tell us the task. We will tell you honestly whether AI fits and how we would build it.