Services

Enterprise AI services built for production.

Every engagement starts with a business problem and ends with a measurable outcome — not a demo.

4–10 weeks typical

Business problem: Teams need production LLM products, not demos — with memory, tools, and reliable outputs.
Solution: End-to-end LLM apps using GPT-4, Claude, Gemini, or open-source models — with RAG, tool use, and memory systems built for production.
Business outcome: Shippable AI features with observability, evals, and clear ownership.

Technologies

4–8 weeks typical

Business problem: Manual multi-step workflows burn hours and break under volume.
Solution: Multi-step AI agents that reason, plan, and act across APIs, databases, and tools — from research agents to business automation.
Business outcome: Autonomous workflows that reduce ops load and cycle time.

Technologies

3–8 weeks typical

Business problem: Invoices, bank statements, and contracts stay trapped in unstructured formats.
Solution: AI extraction, validation, and automation pipelines that turn documents into structured, actionable data.
Business outcome: Hours of manual entry eliminated; fewer GST/credit errors.

Technologies

4–8 weeks typical

Business problem: AI sits outside Salesforce, ERP, and CRM — so adoption stalls.
Solution: Connect AI to your stack via MCP servers, APIs, and event-driven pipelines with zero disruption.
Business outcome: Natural-language access to live business systems.

Technologies

8–14 weeks typical

Business problem: Founders need a full AI product — UI, API, infra — not a prototype.
Solution: Full-stack builds: React + FastAPI + LLM layer + cloud, delivered as working SaaS.
Business outcome: Production SaaS with clear milestones and launch path.

Technologies

2–4 weeks typical

Business problem: Teams don't know where AI will create real ROI.
Solution: AI readiness assessment, stack evaluation, and a 90-day adoption roadmap.
Business outcome: Prioritized opportunities with architecture and cost clarity.

Technologies