Production AI systems with data pipelines, evaluation loops, monitoring, and human review built in.
S&P Global reports 42% of companies abandoned their AI initiatives in 2025. Only 1% have scaled beyond pilots. The gap between an impressive prototype and a reliable production system is enormous, and most AI projects stall in it. Fourteen years of shipping real software is what closes it. We're model-agnostic and platform-agnostic, picking the right tool for each problem.
AI strategy & use case identification
Grounded in data readiness, not wishful thinking. We audit your data, your systems, and your org's actual capacity for AI adoption, then identify the 3-5 use cases with the highest feasibility and ROI. Sometimes the honest answer is "you're not ready yet." We'll say so.
Agentic AI development
Single-agent and multi-agent systems, MCP and A2A protocol implementation, and agent orchestration frameworks. This is the fastest-growing category in enterprise AI: Gartner forecasts 40% of enterprise apps will integrate task-specific agents by end of 2026, up from less than 5% in 2025. We build agents that do real work, not chatbots wearing a trench coat.
RAG system design & implementation
Enterprise-grade retrieval-augmented generation, not a toy demo with a PDF and a prayer. Vector databases (Pinecone, Weaviate, and pgvector), hybrid search strategies, chunking optimization, reranking pipelines, and the evaluation frameworks that tell you whether your RAG system is actually producing good answers or just confident-sounding wrong ones.
LLM integration & fine-tuning
We're model-agnostic. Claude for reasoning. GPT for structured output. Llama or Mistral when cost, latency, or data sovereignty demand open-weight models. We'll tell you when fine-tuning is worth it, when RAG is better, and when a well-crafted system prompt beats both. The answer depends on your data, your latency requirements, and your budget.
Agents and AI workflow automation
The practical stuff that saves real time and money. Document processing pipelines, intelligent routing, automated classification, and extraction and summarization systems. Not every AI application needs a multi-agent architecture; sometimes the highest-ROI solution is a well-designed automation that eliminates 40 hours of manual work per week.
AI governance & responsible AI frameworks
The Colorado AI Act takes effect June 2026. The EU AI Act high-risk provisions go live August 2026. We build compliance into the architecture from day one: risk assessments aligned to NIST AI RMF, bias testing, drift monitoring, audit trails, and the documentation that keeps your legal team sleeping at night. Governance isn't a checkbox; it's infrastructure. See our AI Governance commitments for how we protect your data.
Senior judgment at every step. AI and humans doing what each does best.
BCG's 10-20-70 rule says it plainly: algorithms account for 10% of AI project success, technology and data for 20%, and people and processes for the remaining 70%. That 70% is the entire reason our Conductor model works when pure AI consultancies don't. The person who designs your AI system is the person who owns the client relationship, manages the delivery team, and ensures the system actually works in production.
Your Conductor evaluates the problem and designs the architecture
Model selection, data pipeline design, inference infrastructure, monitoring strategy, and human-in-the-loop patterns. These decisions are made by a senior engineer-architect who's been shipping production systems for over a decade. The first question they'll ask isn't "which model should we use?" It's "does this problem actually need AI?" Sometimes the answer is no. A well-designed database query, a rules engine, or a good integration might solve it better and cheaper. We'd rather tell you that upfront than sell you something you don't need. And if the thing that moves your EBITDA is an unglamorous database migration, we're happy to do that instead.
AI agents handle prototyping and repetitive implementation
Code scaffolding, test generation, documentation, boilerplate integration code, config files, and evaluation harness setup. Our Conductors practice context engineering: the systematic discipline of feeding AI tools exactly the right information to produce production-quality output. This isn't prompt engineering tricks. It's maintaining structured client context repositories, “AGENTS.md” files per engagement, and persistent context pipelines that “fine-tune” the model’s knowledge for better outcomes per turn.
Senior specialist ML and data engineers handle the complex work
Model training and fine-tuning, advanced RAG pipeline optimization, streaming inference architecture, MLOps infrastructure, and custom evaluation frameworks. Our engineer bench includes senior ML and data engineers vetted through paid working sessions, each with deep experience on the specific platforms and model families your project requires. They work alongside the Conductor, not as detached resources on a separate track.
Production deployment with monitoring, governance, and clean handoff
Drift detection, fallback mechanisms, observability dashboards, governance compliance documentation, and operational runbooks are built during construction. Not the last week. When the engagement ends, your team owns everything: the models, the pipelines, the infrastructure, and the playbook. And the system is designed to evolve, because AI systems that can't be updated are AI systems that decay.
What this model actually looks like in practice.
Here's what that buys you. Faster delivery means lower cost and faster time to value. AI-generated tests mean fewer production defects. AI on the boilerplate means your senior people spend their hours on judgment, not typing.
We're clear about how long engagements take and who's on the team. Strategy and pilots are fixed-scope, fixed-price. Full implementations use a hybrid model: base fee plus outcomes tied to measurable results. We'll talk through investment once we understand your use case and scope.
The cost that matters most is the cost of getting AI wrong: the pilots that never reach production, the spend that buys a demo instead of a system, and the team that quietly goes back to the manual process while the tool gathers dust. Building it right the first time is usually smaller than the cost of getting it wrong, and it's the one that actually keeps paying back.
We're a good fit when you've moved past the hype and need AI that actually works.
Most of our AI clients have a story. They tried something, it didn't work, and now they're skeptical. Good. Skepticism is the right starting posture. Here's who gets the most from working with us.
Companies that tried AI and got burned
The pilot looked great in the demo. Then it met real data, real users, and real edge cases. You spent six figures and have nothing in production to show for it. You're not anti-AI; you're anti-waste. You need a partner who starts by asking whether your data is ready, not whether your budget is big enough.
Organizations with clean data ready for AI
You've done the hard work: your data platform is modern, your pipelines are reliable, and your governance is solid. Now you want to put AI on top of that foundation. You're the ideal starting point. The high failure rate doesn't apply to you because you've already solved the prerequisite problem.
CTOs tasked with "an AI strategy"
The board wants AI. Your CEO read an article. You know it's more complicated than anyone in the C-suite thinks, and you need a partner who'll tell the truth about what's realistic, what's not, and what has to happen before any of it works. We'd rather lose a deal by being honest than win one by overselling.
PE portfolio companies with AI in the thesis
The investment thesis includes AI-driven value creation. The operating partner needs measurable results for the next board meeting: use cases scored, prerequisites named, risks visible, and delivery tied to business outcomes instead of model accuracy theater.
Great fit
- Organizations with clean, connected data ready for AI workloads
- Companies that tried AI, got burned, and want a realistic path to production
- CTOs who need scored, defensible use cases for the board
- PE portfolio companies with AI-driven value creation in the thesis
- Teams that need governance baked into the architecture, not bolted on after
Not the right fit
- Companies whose data infrastructure is broken (start with Data Engineering first)
- Teams looking for a chatbot wrapper or a quick AI demo to impress the board
- Organizations that want AI strategy without being willing to hear 'you are not ready'
- Projects with budgets under $25K for the assessment phase
Not sure where AI fits? That's the right question to start with.
The AI Opportunity Assessment is a fixed-scope, fixed-price engagement that evaluates where AI can deliver measurable value in your organization. We audit your data readiness, map your systems, and identify 3-5 high-impact use cases with real feasibility and ROI analysis. It's the diagnostic before the investment.
Here's the honest part: the assessment might tell you your data isn't ready yet. If so, it'll tell you exactly what to fix first and how long it'll take. We'd rather do that work and build AI that actually works than sell you a shiny prototype on a broken foundation.
Explore the AI Opportunity AssessmentAlready know what you need?
If your data is ready and you have a specific AI use case in mind, we can scope a pilot or full implementation directly. Every project starts with a conversation. Tell us what you're working on.
AI engagements connect to everything else. That's by design.
AI systems don't exist in a vacuum. They surface data quality issues that need fixing. They need user interfaces to be useful. They need ongoing governance and strategic oversight. The same Conductor who built your AI system sees what comes next, with full context carried forward.