Domain Intelligence
Your domain experts configure, evaluate, and improve AI systems directly — no PhD required. The intelligence belongs to your team, not a vendor.
Expert-Driven Configuration
Your domain experts — not engineers — configure AI behaviour through intuitive interfaces. Business rules, evaluation criteria, and quality standards stay in the hands of people who understand the domain.
Evaluation Frameworks
Built-in evaluation pipelines let your team measure AI quality against domain-specific criteria. Know exactly when the system is performing well and when it needs adjustment.
Continuous Improvement
Domain intelligence compounds over time. Every correction, every edge case, every new rule makes the system smarter. Your competitive advantage grows with each interaction.
No Black Boxes
Full transparency into how AI decisions are made. Audit trails, explainability, and human oversight baked in from day one — not bolted on as an afterthought.
FAQ
Frequently asked
What does 'domain intelligence' actually mean in practice?
It means building an AI system where the people who understand the business — your underwriters, claims adjusters, clinicians, dispatchers, whatever the domain is — can tune the model's behaviour directly. Instead of an ML team round-trip every time the rules change, your experts edit prompts, add evaluation cases, adjust thresholds, and approve edge-case handling through interfaces designed for them.
How is this different from just buying a generic LLM API?
A raw LLM gives you a generic model. Domain intelligence is the wrapping around it: the prompts that encode your business rules, the evaluation suite that tells you when quality regresses, the human-in-the-loop pathways for edge cases, and the audit trail that lets you defend a decision in a regulated environment. The LLM is the engine — the domain intelligence is everything that makes it actually usable for your business.
Who owns the AI configuration after the engagement ends?
You do, completely. Prompts, evaluation sets, rule definitions, and the interfaces your experts use to manage them all live in your repository, on your infrastructure, with your accounts. We design the system so a future provider — or your in-house team — can take over without re-architecting anything. No vendor lock-in to N90 or to any model provider.
What if our domain experts aren't technical?
That's the entire point. The configuration interfaces are designed for the same people who currently do the work — typically zero-code, with plain-language prompt editing, side-by-side comparison of model outputs, and one-click rollback. Engineers are still needed for the underlying infrastructure, but the day-to-day intelligence is yours to evolve.
How do you handle hallucinations and bad model outputs?
Three layers: an evaluation suite that runs on every prompt change and flags regressions, a human-review pathway for low-confidence outputs, and structured audit logs so every decision can be traced and explained. We treat hallucinations as a property to be measured and constrained, not a problem to be hidden behind disclaimers.
What model providers do you work with?
We are model-agnostic by design and most production systems we build are routed through a gateway (typically the Vercel AI Gateway or similar) so you can switch between Anthropic, OpenAI, Google, and open-source models without code changes. We have particular depth with Claude — including Claude Code for engineering workflows — but the architecture supports any frontier model.
Ready to unlock your domain expertise?
Tell us about your domain and the decisions your team makes. We'll design the intelligence layer.
Start a conversation