Services
We don't write code for you. AI does that now. We make sure your AI-powered efforts actually succeed—by bringing the judgment, design, and orchestration that no model can provide on its own.
Agent Workforce Design
The Problem
Companies are deploying AI agents across sales, support, operations, and engineering—but nobody's designed how these agents relate to each other or to human roles. The result is chaos, duplication, and critical decisions being made by systems nobody's overseeing.
What We Do
We design your hybrid workforce—mapping which tasks should be fully autonomous, which need human oversight, and which should stay entirely human. Think of it as organizational design for a company that's part human, part AI.
This Includes
- Mapping workflows against an autonomy spectrum (human-in-the-loop → human-on-the-loop → human-out-of-the-loop)
- Defining decision rights: what agents can decide alone vs. what needs approval
- Designing escalation paths when agents hit edge cases
- Rethinking roles and teams around AI capabilities
Why this matters: HR consultancies don't understand the tech. Tech consultancies don't understand org design. We sit at the intersection.
Multi-Agent Orchestration
The Problem
Your company has a Salesforce Agentforce instance, a custom Claude integration, an n8n automation layer, and three teams building their own AI tools. None of them talk to each other. Data is siloed. Workflows break at the handoff points.
What We Do
We design and implement the orchestration layer—connecting your agent ecosystem using open protocols like MCP and A2A so your AI systems actually work as a system.
This Includes
- Agent inventory and capability mapping
- Integration architecture using MCP, A2A, and API protocols
- Shared memory and context design across agent boundaries
- Conflict resolution frameworks when agents disagree
- Monitoring and observability for multi-agent workflows
Why this matters: This is the 'integration consulting' of the AI age—but at the protocol level, not just the API level. Gartner reports a 1,445% surge in multi-agent system inquiries. Every company will need this.
AI Quality & Trust Review
The Problem
Your team (or your vibe-coding founder) built a product with AI in a weekend. It works. It looks great. But is it secure? Is the data model sound? Will it scale? Does it comply with regulations? Nobody knows, because the codebase is a black box.
What We Do
We review AI-generated systems—not line by line (that's the old way), but structurally. We evaluate architecture decisions, security posture, data flows, and failure modes. Think of it as a building inspection for AI-built software.
This Includes
- Architecture review of AI-generated codebases
- Security and data flow assessment
- Scalability and failure mode analysis
- Integration integrity checks
- Compliance gap analysis
- Actionable recommendations (not 100-page reports)
Why this matters: The faster people build, the more they need someone to tell them if what they built is actually good. We're the second opinion that prevents expensive mistakes.
Outcome Engineering
The Problem
Your agents are running. But are they delivering value? Software is shifting from 'vibe coding' to what IBM calls 'Objective-Validation Protocol'—where humans define goals and validate while agents execute. But most companies have no framework for defining success or measuring it.
What We Do
We help you define what 'good' looks like in measurable terms, then design the validation loops to ensure your autonomous systems are actually delivering.
This Includes
- Defining success metrics for AI-driven workflows
- Designing validation and feedback loops
- Building monitoring dashboards for agent performance
- Creating escalation triggers when outcomes drift
- Continuous improvement frameworks
Why this matters: Without outcome engineering, you're running agents on vibes. That works for a demo. It doesn't work for a business.
AI Enablement Sprints
The Problem
Your team knows AI is important but they're stuck between 'playing with ChatGPT' and actually transforming how they work. They need to become effective 'agent bosses'—people who can direct, evaluate, and orchestrate AI tools—but a 6-month training program is too slow.
What We Do
Short, intense engagements (1-2 weeks) that give your team the skills and frameworks to work effectively with AI. Then we leave and you're self-sufficient.
This Includes
- Hands-on workshops with real AI tools (Claude Code, MCP, agent frameworks)
- Building actual workflows together, not slideware
- Establishing team-specific CLAUDE.md / project conventions
- Prompt engineering and output evaluation skills
- Framework for evaluating and adopting new AI tools as they emerge
Why this matters: The companies that win won't be the ones that hire AI consultants forever. They'll be the ones whose teams learn to be great at working with AI. We accelerate that.
On-Call AI Advisory
The Problem
You don't need a consulting project. You need someone to call when you're about to make a big AI decision. 'Should we replace our support team with agents?' 'We built this with Claude Code—is the architecture sound?' 'Our competitor just shipped an AI feature—how should we respond?'
What We Do
A retainer-based advisory relationship. Fast, high-trust, low-overhead. You bring the questions, we bring the judgment.
This Includes
- Asynchronous access for quick-turn questions
- Monthly architecture or strategy review calls
- 'Sanity check' reviews on major AI investments
- Technology radar updates relevant to your industry
- Priority access for urgent decisions
Why this matters: The traditional model of project-based consulting is too slow for a world that moves this fast. You need a trusted advisor on speed dial, not a 6-month engagement.
Not sure what you need?
Every company's AI journey is different. Let's talk about where you are and what would actually move the needle.