AI consulting services
AI consulting
Founder-led AI consulting for workflow scope, architecture decisions, model choice, and delivery planning before a build starts.
Explore AI consultingApplied AI services
AI services for teams that need clearer workflow scope, useful systems, and production delivery
These pages cover the main ways companies usually engage the practice: clarifying the right use case, building document or knowledge workflows, and making them reliable enough for real work.
Scope the right AI opportunity before engineering time is wasted.
Build document, knowledge, or workflow systems around real operating constraints.
Keep production concerns such as evaluation, review, and observability visible from the start.
Service pages by business need
Each page focuses on a specific business need and links to related services so teams can find the right scope faster.
Applied AI engineering
Applied AI engineering
Hands-on applied AI engineering for production features, internal tools, document workflows, transcription flows, and business processes built around real data and constraints.
Explore AI engineeringRAG systems
RAG systems and knowledge assistants
RAG system design and implementation for enterprise search, internal knowledge assistants, and source-grounded answers.
Explore RAG systemsAI agents
AI agents and agentic workflows
AI agent design and implementation for tool-using workflows, human-in-the-loop operations, and multi-step automation.
Explore AI agentsDocument intelligence
Document intelligence
Document intelligence services for classification, extraction, review workflows, and source-backed document operations.
Explore Document intelligenceWorkflow automation
AI workflow automation
Workflow automation services for internal operations, AI-assisted processes, speech-to-text or reporting flows, approval loops, and business task acceleration.
Explore Workflow automationHow engagements usually start
Most projects begin with a short scoping conversation, then move into implementation only after the use case, the constraints, and the likely delivery path are clear.
- 1Clarify the workflow, users, source data, compliance constraints, and success criteria.
- 2Choose the right architecture across models, retrieval, tooling, and human review.
- 3Ship a focused implementation that can be tested quickly in a real operating context.
- 4Add evaluation, observability, and iteration so the system can support production use.