We build GPT-powered apps, agents, and automation systems on the OpenAI platform: grounded in your data, evaluated, and monitored in production.
The gap between a GPT demo and a GPT product
A prototype that wows in a meeting often collapses in production: hallucinated answers reach customers, token costs surprise finance, and behavior shifts silently when a model version changes. We close that gap with retrieval grounding, structured outputs, evaluation suites, and cost controls designed in from the start.
What we build with OpenAI
- GPT-powered product features and internal copilots grounded in your data
- Agent workflows with function calling that act across your systems
- Embeddings-based semantic search over documents and records
- Evaluation, cost monitoring, and version pinning for stable behavior
How we work
Define measurable success criteria before writing a prompt
Ground outputs in retrieval, constrain them with structured schemas
Build eval suites that run on every prompt or model change
Instrument cost and latency per feature from day one
Typical stack
Frequently asked questions
Grounding through retrieval over your own data, structured output schemas, and evaluation suites that catch regressions before release. Where a wrong answer is costly we add human review steps rather than pretending the risk away.
Right-sizing models per task, caching, prompt budgets, and per-feature cost dashboards. Most cost blowups come from using the largest model everywhere; most savings come from routing easy tasks to cheaper ones.