01
Enterprise copilots
Role-specific assistants embedded in your existing tools, grounded in your data and your permission model.
Artificial intelligence
Generative AI, engineered for production.
Custom GenAI applications, copilots, and content intelligence built on OpenAI, Claude, and Gemini — with the governance, evaluation, and security that enterprise deployment demands.
What we build
02
Content intelligence
Generation, summarization, and transformation pipelines for documents, support, and knowledge work.
03
Multimodal applications
Text, vision, and voice combined in single workflows — from claims photos to call transcripts.
04
Evaluation & guardrails
Automated eval suites, safety filters, and audit trails that make GenAI safe to ship.
How we deliver
Step 01 — Assess
Use-case discovery, data and platform readiness, and a business case with measurable outcomes for generative AI.
Step 02 — Build
Senior-led delivery in weekly increments — architecture, security, and quality gates baked into every sprint.
Step 03 — Operate
Production monitoring, SLAs, and continuous improvement through our managed services team in Chennai.
Tools & platforms
OpenAIClaudeGeminiAzure OpenAILangChainLlamaIndexPineconeFastAPIPython
In depth
Why enterprises are moving generative AI from pilots to production
Most organizations have already run a generative AI pilot. Far fewer have shipped one. The gap is rarely the model — it is evaluation, governance, data security, and integration with the systems where work actually happens. Our generative AI development practice exists to close that gap: we build copilots, content-intelligence pipelines, and multimodal applications with the engineering discipline of an enterprise platform, not a demo.
A production GenAI system starts with grounding. We connect models to your documents, warehouses, and APIs through retrieval-augmented generation so answers cite your data — not the open internet. Every deployment ships with automated evaluation suites that measure accuracy, safety, and tone before and after each change, and audit trails that satisfy compliance teams in healthcare, banking, and government.
Model strategy stays flexible: OpenAI, Claude, and Gemini through hardened gateways, or private models inside your perimeter when data residency demands it. Because we also run cloud infrastructure and managed services, the system you launch keeps improving after go-live — with cost, latency, and quality tracked as first-class metrics.
Grounded, not generic
RAG pipelines connect models to your documents and warehouses, with citations and permission-aware retrieval.
Evaluated before shipped
Automated eval suites score accuracy, safety, and tone on every release — no silent regressions.
Model-flexible by design
OpenAI, Claude, Gemini, or private LLMs behind one gateway, so you are never locked to a vendor.
Frequently asked questions
Have a different question? Talk to an engineer, not a salesperson.
How long does a generative AI project take?
Discovery to working pilot typically takes 4–6 weeks. Production hardening — security review, evaluation suites, and integration — usually adds 8–12 weeks depending on compliance requirements.
Can our data stay private?
Yes. We deploy via private endpoints with no-training agreements, or fully self-host open models inside your cloud so nothing leaves your perimeter.
How do you measure output quality?
Golden datasets and automated evaluation runs on every change, human review loops for high-risk flows, and business-metric dashboards agreed before the build starts.