Private AI systems,
built the way you'd build them
if you had the time.

A law firm in Bangalore had 12,000+ contracts spread across SharePoint, shared drives, and email attachments — nobody could find anything, and renewal deadlines kept slipping. We built a system that ingested the entire corpus, extracted key terms, renewal dates, obligations, and deadlines automatically, and made it all searchable with cited, source-linked answers. Role-gated access so partners see everything, associates see their practice area. Plugged straight into their existing SharePoint and Google Drive setup.

A fintech company in Singapore had five teams all hitting OpenAI directly with no visibility into cost, no fallback when the API went down, and no way to experiment with other providers without rewriting application code. We built a gateway that sits between their apps and every model provider — OpenAI, Anthropic, Google, their own self-hosted Llama instances. Routes each request based on task type, cost ceiling, and latency. Token-level usage tracking, per-team budgets, automatic failover, and a prompt versioning layer so they can A/B test without redeploying. They've since swapped primary providers twice without touching a single application.

A market research firm in Mumbai was spending 40+ analyst hours per week compiling reports from public filings, news feeds, and internal databases — copy, paste, summarize, repeat. We built an agentic system that does it autonomously. Multi-step AI agents plan the research, pull data from a dozen sources, cross-reference findings, draft structured reports, and flag anything that needs a human eye before publishing. The orchestration layer handles tool calls, retries on failure, manages context across long-running jobs, and includes human-in-the-loop checkpoints for high-stakes outputs. What took a week now takes overnight.

A healthcare software company in Pune with 80 engineers wanted Copilot-level code review but couldn't send their codebase to external APIs — regulatory and IP constraints made it non-negotiable. We're building them a private alternative that runs entirely on their infrastructure. Reviews PRs against their own style guides and architectural patterns, flags security issues, suggests refactors, and generates context-aware documentation. The model is fine-tuned on their codebase, not the public internet. Hooks into GitHub Enterprise via webhooks. Every suggestion is traceable, auditable, and never leaves their network.

A defence contractor in Germany needed document intelligence across classified operational files but couldn't use any cloud-based AI tool — data residency and audit requirements made it impossible. We deployed the entire system inside their air-gapped network. Open-source models running locally via vLLM, encrypted credential storage, complete audit trails on every query, and identity integration through their existing SAML provider. Zero external dependencies. Passed their internal security review on the first submission.

A logistics company in Chennai had a back-office team of four people whose entire job was checking a shared folder for new shipment documents, opening each one, typing the key fields into their management system, and filing the original. Every day, dozens of PDFs. We automated it end to end — pipelines watch their Google Drive and SharePoint for new uploads, classify each document on arrival, extract structured fields, and push the data straight into their downstream systems. The team now handles exceptions instead of data entry.

An international relocation company in Dubai needed a single system that could think across their entire operation. We built a private AI platform with a unified knowledge layer over operational documents, policies, and regulatory filings — hybrid search, retrieval-augmented Q&A, all running inside their own VPC. Now expanding into quoting, booking, finance, CRM, and move management, consolidating three commercial SaaS products into one company-owned platform.