AI-Powered Solutions for Real Business Problems

A typical engagement covers use-case scoping, data and retrieval design (RAG, vector stores, or fine-tuning where warranted), model selection and prompt/agent engineering, integration with your existing systems, and production concerns — evaluation, observability, cost controls, and guardrails — so the system is monitored and maintainable, not a one-off demo.

Cost is driven mainly by data volume, the number of systems it needs to integrate with, and how much evaluation and guardrail work the accuracy bar demands — not by lines of code. We scope a fixed estimate after a short discovery call rather than quoting from a generic package, since two RAG pipelines can differ in cost by several multiples depending on those factors.

A focused proof-of-concept is typically the fastest phase. Hardening it for production — evaluation harnesses, monitoring, fallback handling, and cost tuning — is usually the longer stretch, and its length depends on how much the accuracy and reliability bar demands. We favor shipping a narrow, real use case first, then expanding, over a long pre-launch build.

Yes. Roughly half of our AI engagements are with US-based startups and enterprises, alongside teams across India, the UAE, and the UK. We overlap working hours with US Eastern and Pacific time zones and run async-friendly delivery — written specs, recorded demos, and shared dashboards — so distance doesn't slow decisions down.

We build on OpenAI, Anthropic's Claude, Google Gemini, Meta's Llama, and open-weight models self-hosted where data residency requires it, served through AWS Bedrock or Azure OpenAI. We choose the model per use case — latency, cost, and accuracy trade-offs — rather than defaulting to one vendor, and design integrations so swapping models later doesn't mean a rebuild.

We scope what data reaches a model at all — redaction and minimization before retrieval, tenant isolation in vector stores, and audit logging on every prompt and response. For regulated data (health records, financial data), we favor private endpoints or self-hosted models over public APIs, matching the client's compliance requirements case by case.

You own the code, infrastructure, and model configurations from day one — nothing is licensed back to us. At handover we deliver documentation, an architecture walkthrough, and a transition period where your team pairs with ours before we step back, so you can run and extend the system independently.