Software augmented by AI, built to stay up.
AI engineering is ordinary application engineering, with a model handling the steps that need judgment and everything else staying deterministic. It is the discipline of building software applications augmented by artificial intelligence.
Most of the work is therefore not the model. It is the layer around it: the APIs it calls, the data it reads, the queues that retry, the checks that catch a wrong answer before a customer sees it, and the alert that reaches a person at 2am. That same layer already carries your integrations, pipelines, and hosting, so we build it on purpose, document it, and run it in your cloud accounts.
How it works in practice
Audit what exists
Every integration, webhook, and cron job inventoried, including the ones nobody remembers building.
Design the backbone
One platform layer instead of point-to-point spaghetti: events in the middle, systems on the edges.
Put the model where it pays
The model handles the steps that need judgment, reading documents or drafting replies. Deterministic steps stay deterministic.
Build for failure
Retries, dead-letter queues, dashboards, and paging designed in from day one. Things break and customers should not notice.
Check the output
Evals on real cases, rules on what an agent may do unsupervised, and a person on anything ambiguous or high-stakes.
Document and hand over
Runbooks and architecture docs in your repo, so the system outlives any one engineer, including us.
Foundation showing cracks?
Webhook spaghetti
Point-to-point connections nobody documented.
Silent failures
Integrations break and nobody is paged.
Scale surprises
Rate limits and timeouts at exactly the wrong moment.
Vendor lock
Logic trapped in tools you cannot extend.
The demo that never shipped
An impressive prototype that no team will put in front of a customer.
Answers nobody checks
An agent writes to your systems and nothing verifies what it wrote.
What we build
Event backbone
Queues, workers, and an event log in the middle, so systems stop calling each other directly.
API integrations
CRM, billing, support, and ads connected through their official APIs. No browser bots, no fragile exports.
Data pipelines
ETL that keeps the same record correct in every system, with history you can replay.
Agent design and orchestration
Agents scoped to one job, with the tools, context, and rules for when they hand back to a person.
Evals and guardrails
Test sets built from your real cases, so a prompt change gets measured instead of guessed at.
Monitoring and runbooks
Dashboards, paging on every critical path, and written procedures for the failures worth planning for.
The 2am alert reaches us first.
Documented, monitored, and running in your cloud accounts, with failures paging us before your customers notice.
The tools behind it
Built on models and infrastructure proven in production, plugged into the stack you already run.
Claude Code
OpenAI
AWS
LangChain
PostgreSQL
Redis
Docker
Node.jsClaude CodeOpenAIAWSLangChainPostgreSQLRedisDockerNode.jsClaude CodeOpenAIAWSLangChainPostgreSQLRedisDockerNode.jsReady to move past the demo?
Name the integration that breaks most often. We'll start with that one.