RAG assistants
Ask questions across contracts, manuals, tickets or past proposals. Every answer cites the source passage so a user can check it in one click.
Practical AI, not demos · Issue #08
AI Development Company in India
We build AI systems that do one job inside a real workflow — answering questions over your documents, drafting proposals, extracting invoice fields, summarising meetings — and we measure whether they actually work.
We usually reply within one business day. No obligation, no sales sequence.
At a glance
- Projects from
- USD 3,000
- Typical timeline
- 3–10 weeks
- Based in
- Pune, India
Fixed scopeWeekly demosYou own the code
SINCE
2021
2021
- AI practice launched
- 2025AI practice launched
- AI products in production
- 3AI products in production
- Private inference option
- On-devicePrivate inference option
- Countries served
- 10+Countries served
The honest version
Why teams hire us for ai
The gap between an AI demo and an AI product is mostly engineering: retrieval quality, permissions, evaluation, cost control and what happens when the model is wrong. That gap is where we work.
Our builds run on your data, with citations back to the source document and a human review step wherever the output has commercial or legal weight. If the documents do not contain the answer, the assistant is designed to say so rather than invent one.
Based in Pune, we work with clients across India and abroad. We are deliberately model-agnostic — OpenAI, Anthropic, Gemini or open-weight models running on your own infrastructure — so a change in vendor pricing is an engineering decision, not a crisis.
What we deliver
The work, specifically
Every engagement is scoped to your situation, but this is the shape most of them take.
Document automation
Extraction, classification and drafting pipelines for invoices, RFPs, onboarding packs and compliance paperwork.
Private & on-device AI
Local models and on-device inference for teams that cannot send sensitive data to a cloud API — the approach behind our open-source Private AI Assistant.
Support automation
Assistants grounded in your help centre and past tickets, with escalation to a human when confidence is low.
Workflow automation
Connect CRM, email, WhatsApp and spreadsheets, and remove the copy-paste between them with auditable automations.
Evaluation & guardrails
Test sets, confidence thresholds, refusal behaviour and logging, so quality is a measured number rather than a feeling.
The difference
Why not just the cheapest quote?
Because the cheapest quote is usually an estimate of the easy version. These are the things we do that make the second year cheaper than the first.
Accuracy is tested, not assumed
We build a small evaluation set from your real documents and report precision before go-live. If it is not good enough, we say so.
Your data stays yours
No-training API settings, self-hosted open models where needed, and documented data flows. We never repurpose client content.
Built to swap models
A thin provider layer means you can move between vendors or to a local model without rewriting the product.
Human in the loop by design
Drafting tools keep a person in the approval seat. The time saving comes from a strong first draft, not from removing judgement.
Proof, not adjectives
Recent missions
Real products with real screenshots, the challenge, the stack and the outcome.
AI & Automation2025
AI RFP Proposal Platform
A retrieval-augmented proposal generator that drafts tailored RFP responses from a company's own past proposals and client requirements.
ReactFastAPIPythonMySQL
Read the issueAI & Mobile2026
Private AI Assistant
A free, open-source Android app that records meetings, transcribes them with on-device Whisper, summarises them with a local Llama model and answers questions about your meeting history — without sending a single byte to the cloud.
KotlinAndroidWhisperLlama 2.5
Read the issueQuestions, answered
Before you get in touch
The things clients ask us most often at this stage. If yours is not here, email and we will answer personally.
A focused prototype over your own documents starts around USD 3,000–5,000. A production assistant with evaluation, permissions, integrations and monitoring typically runs USD 8,000–25,000. Model and hosting usage is usually a modest monthly cost billed separately.
Not in our implementations. We use API providers with training disabled, or run open-weight models on infrastructure you control. The data flow is documented in writing before the build starts.
Retrieval grounding, mandatory citations, confidence thresholds and an explicit refusal path. We also test with deliberately unanswerable questions to verify it declines instead of guessing.
Yes. For sensitive workloads we deploy open models with local inference, including on-device for mobile use cases. Expect a smaller model and some accuracy trade-offs, which we measure before recommending it.
OpenAI, Anthropic, Gemini and open-weight models such as Llama, chosen per project on cost, latency, privacy and accuracy. The integration layer stays constant if you switch.
Frequently. We can add a retrieval layer, a summariser or an extraction step to a product you already run, without replacing the rest of the stack.
Keep exploring
Related reading
SaaS product developmentMulti-tenant architecture, billing and onboarding for AI-native products.Read moreCustom software developmentHow AI features fit inside a larger custom platform.Read morePrivate AI Assistant case studyAn on-device meeting assistant built with Whisper and a local Llama model.Read more
Kaboom!
Ready to start your project?
Tell us what you want to build. We will reply within one business day with honest advice and a clear next step — no pressure, no jargon.