AI integration
LLM features that survive contact with production.
- Typical scope
- 1–4 AI features with evaluation harness
- Timeline
- 1–4 weeks
- Price band
- $399+
The problem you probably have
The demo was impressive. The production version hallucinates, costs more than expected, and nobody can tell whether it is getting better or worse.
What you get
- Structured output with schema validation — model responses never reach a database column as free text
- Evaluation sets and quality metrics, so "it feels better" becomes a number
- Cost ceilings, caching, and a fallback path for every feature — nothing that stops when a provider has an incident
Typical stack
- Anthropic
- OpenAI
- TypeScript
- Python
- pgvector
Questions on this specifically
How do you stop it from making things up?
Mostly by constraining the job. Retrieval over your own data instead of open generation, structured schemas instead of free text, confidence fields, and a human in the loop anywhere the output touches money or a customer. Prompt instructions alone are not a control.
How do you keep the API costs from surprising us?
Cost ceilings per feature, caching for repeated queries, and routing: the cheap fast model handles the routine case, the expensive one is reserved for what needs it. You see projected cost per thousand operations during scoping — before the feature exists, not on the first invoice from your AI provider.
Is our data used to train the models?
No. We use API tiers where providers contractually do not train on your inputs, and anything sensitive can be redacted before it leaves your system. What data flows where is written into the scope — it is an architecture decision you sign off on, not a default you discover.
How will we know if it is actually working?
An evaluation set built from your real cases, scored on every change. "It feels better" becomes a pass rate you can watch move. The same harness catches regressions when a provider updates a model underneath you — which they do, without asking.
Should we build this, or buy an AI product that claims to do it?
Buy, if a product genuinely fits your workflow — we will name it during scoping and take the smaller integration job instead. Build when the value depends on your data, your process, or controls a generic product cannot give you. The wrong answer is building a worse version of something that exists.
Have a project in mind?
Bring us the problem. StratEdge scopes it, staffs it, manages it, and delivers it — one accountable company from start to finish.