Geminate Solutions adds AI to products used by real users: AI study planning in a CA exam prep app, and AI-generated audiobooks in an e-library platform. We build AI features with the same engineering discipline as the rest of the product: testing, monitoring, cost control and security.

Most AI projects stall between a promising demo and something the business can rely on. That gap is engineering work: reliable answers, controlled costs, safe data and a fallback when the model gets it wrong. That is the part we focus on.

SaaS products adding AI features their customers will actually use

Operations teams buried in repetitive work: data entry, document handling, support triage, reporting

Companies that want a chatbot or assistant that answers from their own documents and data

Teams with an AI prototype that is too slow, too expensive or too unreliable to launch

AI features inside your app: assistants, search, summaries, recommendations and content generation

AI agents that take actions across your tools, with human approval where it matters

Workflow automation that removes manual work between systems

Chatbots and assistants trained on your own documents and data (RAG)

Document processing: extracting, classifying and checking data from PDFs, forms and emails

Integrations with OpenAI, Claude, Gemini and open-source models

Rate limiting and cost controls on every AI endpoint

Evaluation and testing, so answers stay reliable as prompts and models change

Your data stays private: access control, no training on your data, and audit logs

Fallbacks and human review for decisions that matter

Monitoring of quality, speed and cost after launch

Project Blueprint (1 week). We pick the highest-value use case, choose the model and estimate running costs before building.

Prototype on your real data. A working version you can test early, so you see quality before committing to the full build.

Production build in two-week sprints. Integration into your product, guardrails, tests and monitoring.

Launch and tune. We track answer quality and cost, then improve both.

Python and Node.js, OpenAI, Claude and Gemini APIs, open-source models, vector databases, and the queues, caching and monitoring around them, on your cloud.