Production AI. Shipped, not demoed.
We design, build and run LLM applications, AI agents and fine-tuned models for companies that need them to work on Monday — with evaluation, cost and an audit trail from day one.
Free 30-minute scoping call. Written estimate within 5 working days.
Any model · Your cloud · Evaluation-first · No training on your data
Illustrative interface. Values are sample data.
OpenAI
Anthropic
Hugging Face
Google Gemini
Meta Llama
Mistral AI
PyTorch
AWS
Google Cloud
Microsoft Azure
By the numbers
* Sample figures for layout review. Replace with audited numbers from engagement reports before launch.
Three systems. One evaluation harness.
Every build ships with the tests that prove it works, the dashboard that shows what it costs, and the trace that explains what it did.
Agents that do the work, with a trace you can replay.
Multi-step agents that call your tools, wait for approval where it matters, and log every decision so support and compliance can see exactly what happened.
- Tool calling against your CRM, ERP, ticketing and internal APIs
- Human-in-the-loop gates on any step that spends money or sends mail
- Replayable runs: same inputs, same trace, for debugging and audit
Illustrative interface. Values are sample data.
Answers grounded in your documents, cited.
Retrieval systems over contracts, manuals, tickets and wikis. Every answer points at the passage it came from, and every miss is logged so the corpus gets better.
- Hybrid search: dense + keyword + reranking, tuned on your queries
- Permission-aware: users only retrieve what they can already read
- Faithfulness and recall measured on a held-out set, every release
Illustrative interface. Values are sample data.
Smaller models, fine-tuned on your data, served on your terms.
When an API model is too slow, too expensive or can't leave your network, we fine-tune an open-weight model on your examples and run it where you need it.
- Data curation, synthetic augmentation and label QA before any training run
- Side-by-side evals against the API baseline on your task, not a leaderboard
- Serving on your cloud, on-prem or edge, with autoscaling and rollbacks
Illustrative interface. Values are sample data.
Custom LLM applications
Copilots, assistants and internal tools on any model, with streaming, memory and guardrails.
Computer vision
Detection, OCR, inspection and video pipelines, deployed to edge devices or cloud.
MLOps & infrastructure
Evaluation harnesses, monitoring, drift alerts and cost dashboards for systems you already run.
Seven industries. The same discipline.
Different data, same method: an evaluation set first, a system your operations team can trust, and a cost line you can read.
Mobility.
Dispatch, ETA and rider-support agents for ride-hailing and fleet platforms.
ETA models · Demand forecasting · Support agentsLogistics.
Route optimisation, document extraction and exception handling across the shipment lifecycle.
Route optimisation · Document AI · Exception triageFinance.
KYC document extraction, fraud signals and customer assistants that stay inside compliance rules.
KYC extraction · Fraud signals · Compliance copilotsReal estate.
Listing enrichment, valuation models and lead-qualifying agents for brokerages and proptech.
Valuation · Listing enrichment · Lead agentsRetail / Manufacturing.
Demand forecasting, visual inspection and inventory copilots on the shop and factory floor.
Forecasting · Visual inspection · Inventory copilotseCommerce / Consumer goods.
Product search, personalisation and post-purchase support agents that cite the order.
Semantic search · Personalisation · Support agentsTravel.
Itinerary assistants, dynamic pricing and disruption-handling agents for OTAs and operators.
Itinerary agents · Pricing · Disruption handlingLet's discuss your industry.
If the data is messy and the stakes are real, the method still applies. Bring the workflow; we'll bring the evaluation set.
Start a project →Three steps, then it's live.
Fixed-scope discovery, weekly demos during the build, and a handover or a managed run. You see working software every Friday.
Discovery sprint.
Two weeks. We map the workflow, pull a sample of real data, and build the evaluation set first — so "done" has a number attached before a line of product code exists.
2 weeks · fixed fee · eval set + architecture + estimateBuild in the open.
Your repo, your cloud, our engineers. A demo every Friday against the eval set, with latency and cost on the same screen as accuracy.
4–12 weeks · weekly demo · your repositoryShip and run.
Handover with runbooks and a trained team, or we run it: monitoring, drift alerts, model upgrades and a monthly cost review.
Handover or managed · SLA-backed · monthly reviewThe demo was easy. Monday is the hard part.
Most AI projects stall between a promising notebook and a system the business can rely on. That gap is the whole job.
- Works on the ten examples in the slide deck
- Nobody can say what it costs per request
- A prompt change breaks something a week later
- Customer data is pasted into a third-party console
- Scored on a held-out set that mirrors production traffic
- Cost per request on the dashboard, with a budget alert
- Every prompt and model change runs the evals before deploy
- Data stays in your VPC; no vendor trains on it
Evaluation-first.
The test set is built before the product. Accuracy, latency and cost are tracked per release, and a regression blocks the deploy.
Your data stays yours.
Built in your cloud account, encrypted in transit and at rest, with model providers configured for zero retention. We hold no copy after handover.
Cost you can see.
Tokens, GPU hours and vendor bills roll up to one number per feature. Routing to a smaller model is a config change, not a rewrite.
Security by default, stack by choice.
We work inside your controls and with the tools your team already runs.
Inside your perimeter.
- Deployed to your AWS, GCP, Azure or on-prem
- SSO, role-based access and audit logs on every system
- PII redaction before any prompt leaves the VPC
- DPA and security questionnaire answered before kickoff
Model-agnostic, by design.
We pick the model per task on your evals, and keep the swap cheap.
The first cohort is being written.
Named case studies publish here with the client's sign-off, real numbers and the honest limits. Want to be one of them?
Talk to us →Start small. Scale when the numbers say so.
Progressive commitment: a fixed-fee sprint first, a scoped build second, a retainer only once something is running.
Two weeks.
Fixed fee · quoted on the call
- Workflow map and data audit
- Evaluation set on your real examples
- Architecture, risks and a written estimate
Scoped delivery.
Per milestone · 4–12 weeks
- Dedicated squad in your repository
- Weekly demo against the eval set
- Production deploy, runbooks and handover
Managed AI.
Monthly · after go-live
- Monitoring, drift alerts and on-call
- Model upgrades run through your evals
- Monthly cost and quality review
* Engagement structures are placeholders pending the published rate card. Every quote is written, fixed for its scope and never guaranteed to produce a business outcome.
Ship your first AI system this quarter.
Tell us the workflow. Within 5 working days you get an evaluation plan, a timeline and a written price — no deck, no commitment.
- Free 30-minute scoping call with an engineer, not a salesperson
- Written estimate, fixed for its scope
- Your data stays in your cloud from day one
Prefer to message directly? WhatsApp +91 7096010005
