Web App Development and AI Systems.
AI Engineering
We build practical AI features with the guardrails needed for real users: grounded answers, streaming UI, cost controls, evals, and fallbacks. Start with one useful workflow, then grow into an AI layer the rest of the product can rely on.
Useful AI patterns
Summaries, search, copilots, classification, and workflows. The useful version is scoped, observable, and tied to a real job users already have.
Summarization
Turn long content, calls, and threads into grounded briefs.
Smart search
Combine semantic and keyword search with citations.
Writing cleanup
Improve tone, grammar, and structure without flattening the voice.
Classification
Route, tag, triage, and moderate with confidence thresholds.
Agents and workflows
Bounded multi-step work with tools, logs, and clear stops.
Internal copilots
Assistants connected to your docs, data, and operational tools.
Content generation
Drafts and variations with structured output and editorial review.
Decision support
Explainable recommendations with the underlying evidence attached.
The production AI layer
A production AI feature is six or seven small systems in a trench coat. Each one has to work, and they all have to fail gracefully.
User Input
Prompt · context · file
App Context
Session · memory · history
Tools / Retrieval
Search · RAG · tool calls
Model Router
Choose model · fall back
Streaming Response
Tokens · UI updates
Cost + Quality
Telemetry · evals
What sits behind the request
Stable prefixes, smart caching
Cache-aware prompt design cuts repeat-prompt cost by up to 90%.
Hybrid retrieval with citations
Semantic plus keyword search, re-ranked, with sources users can verify.
Right model per task
Cheap models for cheap tasks, frontier models where they earn their cost.
Cost and quality, observed
Per-feature budgets, per-request telemetry, and evals before changes ship.
Production concerns
These are the questions any AI feature has to answer before it can be trusted with real users. Most demos skip them. Production code cannot.
01
Hallucination control
Constrained outputs, structured generation, and refusal patterns for high-stakes tasks.
02
Grounding and retrieval
Retrieval pipelines that prefer fresh, cited, and authoritative sources over guesses.
03
Prompt caching
Stable prefixes and cache-aware prompt design cut repeat-prompt spend by up to 90%.
04
Model routing
Cheap models for cheap tasks, frontier models where they earn their cost.
05
Cost tracking
Per-feature budgets, per-request telemetry, and dashboards finance can actually read.
06
Rate limits & fallbacks
Provider outages and quota spikes handled with retries, queues, and graceful degradation.
07
Evals & QA
Golden sets, regression suites, and automated checks before prompts or models change.
08
Streaming UX
Token-by-token rendering, cancel buttons, error states, and partial recovery.
Experience-led AI
AI supports execution, but direction, judgment, and quality remain driven by experience.
Architecture choices
Picking the right shape early saves a rewrite later. The wrong architecture rarely shows up in the demo. It shows up six months in, when costs climb or quality stalls.
Best fit · A clean, focused capability inside an existing product
One provider, one prompt strategy, careful caching. The right starting point when the workload is well understood and latency or cost are not yet the bottleneck.
Best fit · Workloads that benefit from picking the right model per task
OpenRouter or a thin in-house router lets you mix providers, route by cost or capability, and switch models without rewriting features.
Best fit · Multi-step tasks that need tools, memory, and planning
Bounded agents with explicit tools, stopping criteria, and observable traces. Powerful but expensive, so scope and guardrails matter from day one.
Best fit · Anything that has to reason over your own data
Embeddings, hybrid search, re-ranking, and citation-first answer generation. The boring part is the data pipeline. That is also where the quality comes from.
Best fit · High-stakes decisions, regulated content, irreversible actions
AI proposes, humans approve. Designed for review queues, draft-then-approve patterns, and clear audit trails. The model is a collaborator, not an authority.
Put AI to work
If you want something more durable than a prototype, let us design the version that can actually ship.