AI Engineering
AI & intelligent applications, built for production
Most AI features work in a demo and fall apart under real traffic, real data, and real edge cases. Stellar Forge builds the engineering underneath the model — retrieval, evaluation, guardrails, cost control, and monitoring — so the intelligent feature you ship is one you can actually operate.
Where AI projects usually break
A prototype that calls an LLM API is easy. A system that stays accurate as your data changes, stays within budget as usage grows, and fails safely when the model gets something wrong — that's the part that gets skipped under deadline pressure, and it's the part that determines whether the feature survives contact with real users.
Our approach
Grounded, not hallucinated
Retrieval-augmented pipelines and structured context so responses are grounded in your own data, not the model's guesswork.
Evaluated before it ships
Test sets and automated evaluation for accuracy, latency, and cost — so quality is measured, not assumed.
Designed to fail safely
Fallbacks, confidence thresholds, and human-in-the-loop paths for the cases the model shouldn't handle alone.
Cost-aware by default
Model selection, caching, and token budgeting built in from day one, not retrofitted after the first bill.
What's included
- LLM-powered features (search, summarization, generation, classification)
- Retrieval-augmented generation (RAG) pipelines over your own data
- Automation agents for lead scraping, content generation, and workflow tasks
- AI-assisted internal tools (support triage, reporting, data extraction)
- Model evaluation, monitoring, and cost-tracking dashboards
Ready to scope this?
Tell us where things stand today and where you need to get to. We'll respond with an honest read on approach and effort.
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