AI & Machine Learning Development Company in India
Pune-based AI development for RAG assistants, LLM agents and custom ML — production systems with OpenAI, Claude, LangChain and self-hosted pgvector, not demos.
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- ✓ RAG, agents & custom models
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In short
TechLapse is an AI and machine learning development company in Pune, India. We build production RAG systems, LLM chatbots, AI agents and custom ML — including our own NotesAI self-hosted platform and LuminaEdit AI app. Most RAG MVPs ship in 4–8 weeks; full production systems in 1–3 months. Indicative cost: ₹1.5L–₹4L for an MVP, ₹5L–₹15L+ for production. Free scoped quote within 24 hours.
Trusted by teams across India
DropEat · Breezy Mart · Daktrbaboo · Growweb Development · Frugal Innovations
AI solutions for real business workflows
TechLapse builds production AI for Indian businesses — RAG over private documents, LLM chatbots grounded in your data, document intelligence, predictive ML, computer vision and multi-step agents integrated with CRM, ERP and mobile apps.
Self-hosted or cloud RAG over your docs, wikis and databases — with citations, access control and audit trails.
Customer support, internal helpdesks and sales assistants grounded in your data — not generic ChatGPT wrappers.
Extract, classify and route invoices, contracts, forms and medical records — with human-in-the-loop review.
Demand forecasting, churn prediction, anomaly detection and scoring models trained on your operational data.
Quality inspection, object detection, OCR on images and video analytics for manufacturing, retail and logistics.
Custom-tuned models and multi-step agents that call APIs, query databases and execute workflows autonomously.
Off-the-shelf AI tools vs custom AI development
ChatGPT Enterprise and SaaS copilots work for generic tasks; custom AI development wins when you need RAG over private data, self-hosted deployment, ERP integration, DPDP-compliant controls or to own the stack instead of per-seat fees.
You need generic chat, your data can live on a vendor's cloud, workflows are standard and you don't need deep integration, custom guardrails or to own the pipeline.
You need RAG over private docs, self-hosted deployment, fine-tuned models, agent workflows, ERP/CRM integration, compliance controls or you want to own the stack instead of paying per-seat forever.
Quick comparison: ChatGPT Enterprise vs custom RAG vs self-hosted
| Approach | Best for | Data privacy | Typical cost (India, 2026) |
|---|---|---|---|
| ChatGPT Enterprise / SaaS copilot | Generic Q&A, low integration needs | Vendor cloud; check DPA | Per-seat subscription |
| Custom cloud RAG (OpenAI / Claude APIs) | Fast MVP, citations from your docs | API with access controls | ₹1.5L–₹4L MVP + API usage |
| Self-hosted RAG (NotesAI-style, pgvector) | Regulated sectors, DPDP, on-prem | Data never leaves your servers | ₹5L–₹15L+ setup + infra |
A simple, transparent process
Every AI project follows six steps — from a free discovery call to production launch with guardrails, monitoring and optional MLOps retainer. You see working AI in sprints, not a single demo day.
A free call to understand your use case, data sources and success metrics.
Architecture outline, data assessment and honest cost range within 24 hours.
Prompt design, RAG pipeline and integration plan agreed before build starts.
Reviewable progress weekly — with evaluation metrics, not just demos.
Deployment, guardrails, monitoring and team handover — self-hosted or cloud.
Prompt tuning, model updates and new features as usage grows.
What does AI development cost?
Every AI project is scoped individually — but here are honest 2026 starting points for India so you're not guessing. Read the full cost guide →
Single use case, cloud or self-hosted pilot
₹1.5L – ₹4L
RAG + integrations + guardrails + admin
₹5L – ₹15L+
Monitoring, prompt tuning, model updates
₹75K – ₹2L/month
These are indicative ranges, not quotes. Cost depends on data readiness, model choice (OpenAI, Claude, self-hosted) and integration depth — we'll give you a clear, written range within 24 hours of a short call, free and with no obligation.
Production AI we have built
★★★★★ 4.9/5 from 87 reviews · NotesAI RAG platform · LuminaEdit AI photo editor
NotesAI
Self-hosted RAG — document ingestion, pgvector embeddings and cited answers from your private knowledge base.
Read the case study →
LuminaEdit
Generative AI photo editor — Flutter mobile app with on-device and cloud AI processing in production.
View in portfolio →“Bestest work provided by TechLapse and team. A genuinely good company for automation services.”
Growweb DevelopmentAutomation & AI integration“Solid engineering on complex workflows — timely delivery and good communication throughout our AI automation project.”
Frugal InnovationsBusiness automation“TechLapse helped us move from a ChatGPT prototype to a production RAG system our team actually trusts.”
Growweb DevelopmentRAG knowledge baseAI & ML solutions by industry (2026)
We apply RAG, predictive ML and computer vision across healthcare, logistics, retail and B2B SaaS — with compliance and integration patterns suited to each sector.
Clinical document RAG, medical record OCR, appointment triage bots — DPDP-aware architecture.
Demand forecasting, route optimisation ML, invoice extraction and anomaly detection on ops data.
Product recommendation, visual search, support chatbots grounded in catalogue and policy docs.
In-app copilots, sales enablement RAG, AI agents that query CRM and ticketing APIs.
How to hire AI developers from TechLapse
Choose a dedicated AI squad for end-to-end delivery, a fixed-scope MVP for a single use case, or an MLOps retainer once your system is live — all with full IP ownership and NDA available from day one.
Lead AI engineer + backend + QA on your roadmap — best for multi-month RAG or agent platforms.
Locked requirements, phased delivery, clear ₹ range — ideal for a first RAG chatbot or document pipeline.
Monitoring, prompt versioning, cost controls and model updates after launch — keeps AI accurate in production.
From POC to production: MLOps, guardrails & compliance
Production AI needs evaluation metrics, hallucination guardrails, prompt versioning and cost monitoring — not just a working demo. We design for India's DPDP Act with self-hosted options when data cannot leave your boundary.
LLM evaluation suites, latency and token-cost dashboards, A/B prompt tests and rollback — so quality does not drift after launch.
Role-based access, audit logs, data residency choices and NDAs before sensitive docs — self-hosted pgvector when required.
Why TechLapse for AI & ML
We ship real AI products. NotesAI — our self-hosted RAG platform — ingests documents, builds vector indexes and answers from your private knowledge base. LuminaEdit is our generative AI photo editor; DevJarvis is our AI development assistant — all in production, not demos.
Privacy-first architecture. Self-hosted pgvector, on-prem deployment or cloud with strict access controls — chosen per project, not one-size-fits-all.
Full-stack AI engineering. Python, LangChain, Rust backends, PostgreSQL and cloud GPU — not just prompt engineering in a notebook.
You own everything. Models, embeddings, source code and IP are yours — no vendor lock-in, no per-seat trap.
Why work with TechLapse? Download our overview (PDF) See more of our work →Ready to scope your AI solution?
Get a free, no-obligation quote with timelines and a clear cost range within 24 hours.
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How much does AI and machine learning development cost in India?
Indicative 2026 ranges: a focused RAG chatbot or document pipeline MVP typically falls in ₹1.5L–₹4L; production AI systems with integrations, guardrails and admin panels often run ₹5L–₹15L+. Ongoing MLOps retainers are commonly ₹75K–₹2L/month. Exact cost depends on data readiness, model complexity and integration depth — we share a written range within 24 hours.
How long does it take to build an AI or ML solution?
A proof-of-concept or RAG assistant can ship in 4–8 weeks with a dedicated team. Full production systems with evaluation, guardrails and ERP/CRM integrations usually take one to three months. We deliver in sprints so you test real AI features early instead of waiting for one big launch.
What is RAG and when do I need it?
RAG (Retrieval-Augmented Generation) connects an LLM to your private documents, databases or knowledge base so answers cite your data instead of guessing. You need RAG when staff or customers must query internal policies, manuals, tickets or product docs accurately — without training a model from scratch.
What is the difference between RAG and fine-tuning?
RAG retrieves your live documents at query time — best when knowledge changes often and you need citations. Fine-tuning adapts a model's weights to your tone, format or domain language — best when outputs must follow a strict style or specialised vocabulary. Many production systems combine both. Read our RAG vs fine-tuning guide →
OpenAI vs Claude vs self-hosted — which LLM should I use?
OpenAI (GPT-4o) and Anthropic Claude excel at general reasoning and ship fast via API. Self-hosted open models (Llama, Mistral via Hugging Face) keep data on your infrastructure — ideal under DPDP or sector rules. We recommend based on compliance, latency and budget, not vendor preference.
Do you build AI agents?
Yes. We build multi-step AI agents that call REST APIs, query databases, trigger workflows and hand off to humans when confidence is low — using LangChain, custom orchestration or Rust backends depending on scale and latency needs.
How much does generative AI development cost in Pune?
Pune-based teams typically quote similarly to pan-India rates: RAG MVPs from ₹1.5L, production generative AI platforms from ₹5L+. Local delivery saves timezone friction for Indian enterprises; we scope remotely or on-site from our Hinjewadi office.
Will my data stay private if I use AI?
Yes — when architected correctly. We offer self-hosted setups (like our NotesAI platform) where documents and embeddings never leave your infrastructure, plus cloud options with strict access controls. We can sign an NDA before you share anything sensitive.
Can you integrate AI into our existing software?
Yes. We build AI as APIs, microservices or embedded modules — connecting to your CRM, ERP, web app, mobile app or internal tools via REST, webhooks or message queues.
Do I need a large dataset to use machine learning?
Not always. Many projects start with pre-trained models, transfer learning or LLM APIs. For custom ML (forecasting, classification, vision), we assess your data during discovery and advise whether you have enough — or how to collect and label more.
Self-hosted vs cloud AI — which should I choose?
Self-hosted (NotesAI-style) keeps data on your servers — ideal for regulated industries and strict privacy. Cloud APIs (OpenAI, Claude) ship faster and scale easily when data can leave your boundary with proper controls. We recommend based on compliance, budget and latency — not vendor preference.
Who owns the models, code and intellectual property?
You do. Source code, fine-tuned weights, embeddings and IP belong to you. No lock-in to our stack — we hand over repos, configs and deployment docs.
Do you provide support after launch?
Yes. We offer monitoring, prompt tuning, model updates, security patches and new feature development as your usage and requirements grow.
Let's build your AI solution
Free consultation, honest advice, and a clear quote in 24 hours — with no obligation.
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