01
Discovery
We map the workflow, data sources, failure modes, and where a human must stay in the loop. Feasibility is judged against available data and review capacity—not model hype.
Service
AI Development
We build AI features that sit inside real products—intake assistants, classification pipelines, document helpers, and decision support—where correctness, permissions, and operator review matter more than a flashy demo.
Problems this work usually addresses
- Manual review queues grow faster than headcount
- Prototype AI demos fail once they meet permissions, edge cases, or incomplete data
- Teams need evaluation and rollback plans before they widen access
Who benefits most
Founders and product leads who already have a workflow and want AI to reduce friction without handing control to a black box.
Typical outcomes
- Ship AI features that operators can trust in production
- Reduce manual work with guarded automation
- Keep data boundaries, evaluation, and handoff explicit
Technical approach
02
Design & architecture
We choose model providers, retrieval patterns, prompt systems, and tool calling boundaries. Architecture keeps AI outputs auditable and reversible.
03
Build, test, and handoff
We ship behind feature flags, add logging and evaluation harnesses, document operator steps, and leave a clear path to expand coverage after the first release proves itself.
Architecture considerations
Typical systems separate ingestion, retrieval or context assembly, model calls, validation, and persistence. Public surfaces never talk to model APIs without an application layer that enforces auth, rate limits, and redaction rules.
Security
Secrets stay server-side. Sensitive fields are filtered before prompts. Role checks gate who can trigger high-impact actions. Logs capture inputs and outcomes without dumping secrets into client storage.
Scaling
We start with the narrowest workflow that creates value, measure quality, then widen. Caching, batching, and asynchronous jobs come after the review loop is stable.
Technology stack
TypeScriptNext.js / Node APIsOpenAI or Anthropic APIsPostgreSQLQueue / worker patternsObservability hooks
Common challenges
- • Hallucinated actions when tools are too open
- • Cost spikes from unbounded context windows
- • Operators distrusting outputs without evidence trails
Frequently asked questions
Do you train custom models?
Usually no for a first release. Most product needs are better served with strong prompting, retrieval, evaluation, and workflow design. Fine-tuning enters later when volume and labelled data justify it.
How do you decide what should stay human-reviewed?
Anything that changes money, legal commitments, customer-facing decisions, or irreversible records starts with a review gate. Automation expands only after error rates and override patterns are understood.
What does a first AI release usually include?
One primary workflow, clear success criteria, logging, an operator path for failures, and documentation so your team can run the feature without us in the room.
Related reading
Related services
AI Automation
Agent-assisted operations and workflow automation that remove repetitive work without creating black-box risk.
LLM Applications
Retrieval-augmented apps, assistants, and internal copilots grounded in your documents, systems, and permissions.
AI Agents
Task-oriented agents with tools, memory, guardrails, and human checkpoints for real business work.
Workflow Automation
Structured workflow systems that connect people, tools, and approvals across the business.
Relevant projects
Where we commonly deliver this
Have a workflow that should not stay manual?
Describe the process, data sources, and who reviews exceptions. We will tell you what is ready for a first AI release—and what is not.
Discuss an AI build