AI Excellence as a Service
We help engineering teams design the right development process for AI — from domain knowledge extraction through harness engineering to skills and measurement. Our domain modelers know how to organize the flow of business knowledge in an AI-driven world.
The Problem with "AI-Enhanced" Development
Most teams are simply bolting AI tools onto their existing processes. The result? Misconfigured agents, untested skills, and no way to measure what works.
Wrong Focus
Teams obsess over which LLM to use, but the agent is not the variable. Without a deliberate development process — and domain experts who can extract and structure business knowledge for AI — even the best tools underperform.
Skills Gap
AI prompts and workflows are treated as throwaway artifacts. No versioning, no testing, no iteration. Teams reinvent the wheel with every project.
No Measurement
Teams have no way to know if their AI setup actually works. No baselines, no benchmarks, no feedback loop for improvement. Just vibes.
How We Help
Four pillars of AI excellence — from designing the right development process to configuring tools and measuring real outcomes.
Development Process Design
The Right Process Makes Tools Work
Domain knowledge extraction
Our modelers work with your business experts to identify and structure domain knowledge that AI agents need to produce quality code
Agentic workflow design
We design how developers, architects, and AI agents collaborate — clear roles, handoff points, and feedback loops
Team adoption & coaching
Process changes only work when teams adopt them. We coach your developers through the transition to AI-augmented workflows
Continuous process improvement
Regular retrospectives and measurement to evolve your process as AI capabilities and team maturity grow
Harness Engineering
Tools + Context + Instructions = Results
Agent environment audit & optimization
We analyze your current setup and identify configuration gaps that limit agent effectiveness
Tool & MCP server configuration
The right tools connected the right way — code search, LSP, documentation, custom integrations
Context management strategy
Agents need the right information at the right time — we design what context flows where
Knowledge graph experiments (GraphRAG)
Structuring codebase knowledge for more effective agent reasoning
Skills Development
Skills Are Software — Build, Test, Iterate
Team-specific skills
Codified domain knowledge, project conventions, and architectural patterns unique to your codebase
Universal coding skills
Proven patterns for testing, refactoring, and code quality that work across projects
Skill testing & iteration
Activation testing confirms a skill loads. Outcome testing proves it works. We do both.
Distribution across teams
Skills versioned, shared, and maintained like any other engineering artifact
From Meeting to Working Software
Shorter Path from Decisions to Code
Meeting → Design Document
We're shortening the path from team discussions to design documents with sound architectural decisions
Autonomous implementation with verification
AI agents execute from precise specifications with deterministic verification at every step
Deterministic feedback loops
When verification fails, agents receive structured feedback and iterate autonomously
Continuous quality gates
Architecture compliance, test suites, and code standards checked on every change
Measurement & Evaluation
If you can't measure it, you can't improve it. Our evaluation methodology scores AI agent output across multiple dimensions — on real tasks from real codebases.
Real-World Benchmarks
Tasks derived from actual git history and production codebases — not synthetic toy problems. We measure agent performance on the work your team actually does.
Multi-Dimensional Scoring
Not just 'does it compile' — we score architecture compliance, code quality, test coverage, domain modeling, and adherence to team conventions.
Baseline & Progress
Establish where your team's AI setup performs today and track improvement over time as harness and skills evolve. Data-driven optimization, not guesswork.
Feedback Loop
Evaluation results feed directly into harness and skill improvements. Every benchmark run reveals what to optimize next — creating a continuous improvement cycle.
From Meeting to Design Document — Automatically
We're exploring how to shorten the path from team discussions to design documents with sound architectural decisions. Our experimental pipeline extracts decisions, requirements, and architectural patterns — and organizes them using knowledge graph techniques (GraphRAG) into structured design documents.
Meeting Recording
Team discussion, design review, or planning session
AI Extraction
Decisions, requirements, and action items identified
Design Document
Structured design document with architectural decisions, ready for agent execution
Who Is This For?
Our AI excellence services are designed for engineering leaders ready to move beyond "AI-assisted" to truly "AI-native" development.
Product Companies
CTOs · VP Engineering · Engineering Directors
- Teams of 20–200 developers
- Want to measure and optimize AI developer productivity
- Need custom skills for their domain and codebase
Software Houses
Managing Directors · Delivery Managers · Tech Leads
- Want repeatable AI excellence across client projects
- Need to prove ROI of AI tooling with real benchmarks
- Looking for competitive edge through measurable engineering quality
Let's engineer AI excellence together.
Whether you're exploring agentic development or ready to optimize your entire AI engineering stack — we'll help you build the right harness, skills, and measurement. Start with a conversation.