Sprint Studio / Capabilities / AI features
Agentic workflows, RAG, model routing, tool use — shipped as production features inside real websites and web apps. The boring stuff (latency, cost, guardrails, evals, observability) treated as the first-class problem it actually is, not as an afterthought.
Multi-step tool use against your real data and your real systems — not a chat box. Plans, executes, reflects, recovers. Bounded by your guardrails, observable from day one.
Search-augmented generation over your docs, your codebase, or your customer data. Done with the chunking, ranking, and citation handling that decides whether it's a demo or a product.
The right model for the job — small fast model for the 80%, frontier model for the 15%, fallback path for the 5%. Built so cost and latency budgets are knobs you can turn, not surprises you read on a bill.
JSON-schema-shaped responses with validation, retries, and recovery. The boring path from “LLM said something” to “your app did the right thing with it” — done properly so failure modes are visible.
Inline help, copilots, content generation panels — UX patterns that earn their pixels, with prompt engineering, eval suites, and rollback switches behind them. Built into the product, not bolted on.
The eval suite is part of the deliverable. Held-out test sets, regression checks on every prompt change, latency & cost dashboards, content-safety guardrails wired into the deploy gate.
If you skip these, you have a demo. We treat them as the first thing to build, not the last thing to fix.
Token streaming wired through the UI, with measured budgets and routing so you don't ship a feature that takes nine seconds in front of a real user.
Prompt caching, response caching, request batching, model fallbacks — built so cost-per-action is a number you read on a dashboard, not an end-of-month spike.
Input filtering, output validation, prompt-injection mitigations, structured retries, content-safety classifiers — wired in at the seams, not sprinkled on top.
Per-request tracing (prompt, model, latency, cost, eval score), with a dashboard your team uses on day two. Failure modes are visible. Drift is visible.
An eval set is part of the engagement. Prompt changes don't merge without a regression check. New models get an A/B run before they ship.
Every AI feature behind a flag, every prompt versioned, every model swap a config change. When it goes wrong at 11pm, you turn it off — not roll the deploy back.
We've had engagements where the most useful work was talking the team out of an AI feature — usually because the problem was a search box that didn't work, a form that asked for too much, or a workflow that should have been three buttons instead of one chat.
If the problem is solvable with a sensible UI, a real database query, or a piece of plain code that ships in a day, that's what we'll do — even when the brief said “put AI on it.” The point is the outcome, not the demo.