AI Strategy & Implementation

Most consultants handed a Gen AI budget will find a way to use it. We're not most consultants.

AI accelerates what's already there. If your operations are solid, it compounds your advantage. If they're not, it compounds the mess. The firms struggling with AI right now aren't struggling because they picked the wrong model — they're struggling because they handed a powerful accelerant to a dysfunctional process and expected transformation.

We evaluate first. We build second.

What evaluation actually means

Before we recommend anything, we want to understand your actual pain points, your existing data architecture, your team's capacity to maintain what gets built, and whether the problem you're describing actually calls for AI at all.

Sometimes it does. Often, traditional development gets you there more reliably, more cheaply, and without burning tokens on something well-written code would handle in milliseconds. We've reverse-engineered seven years of business logic from a highly customized Salesforce org using AI-assisted analysis — not because AI was the goal, but because it was the right tool for that specific problem. We've also built straightforward integrations where a client came in expecting an AI solution and left with something faster, cheaper, and more reliable. Both outcomes are wins. We have no preference for the one that sounds more impressive in a pitch deck.

If something else does the job better, we'll tell you — and build that instead.

When AI earns its place, here's what we build

RAG Pipelines and Knowledge Infrastructure

Your institutional knowledge turned into a queryable, real-time evolving knowledge base — built on your data, scoped to your domain, returning answers your team can actually trust. We're building Yapout, our own internal multi-agent development platform, on this foundation: it ingests meeting transcripts and Slack threads, enriches requests against a RAG-indexed codebase, and orchestrates parallel agent execution across models. We know how to build RAG infrastructure that stays grounded in your actual data rather than drifting into confident hallucination.

Custom Agents with Domain Expertise Baked In

Not generic chatbots. Agents designed around your specific workflows, your business logic, and your ethos — ones that know when to handle something autonomously and when to put a human in the loop.

For a commercial irrigation field service company, we built an agent trained on their founder's years of equipment knowledge — giving a growing team real-time troubleshooting support in the field without waiting on a callback. The same system processes alert data streaming from equipment made by dozens of manufacturers, normalizing varying structures, codes, and noisy signals into actionable information. It recently surfaced a leak a client didn't know they had, saving thousands of gallons a month. That's what a well-scoped agent actually looks like: domain expertise made available at scale, in context, at the moment it's needed.

We also built BailTies, an agentic research tool for bail bondsmen that extracts relationships, resolves identities, and surfaces aliases from public records — reducing investigations that took over an hour to minutes. Entity resolution at that fidelity requires more than a prompt. It requires careful agent design, deterministic controls, and a clear understanding of where the model can be trusted and where it can't.

Multi-Step Orchestration for Complex Processes

When a process is too nuanced for simple automation but too repetitive for your best people, orchestrated AI workflows close the gap. Reflective pipelines that handle complexity, surface the right information at the right moment, and hand off cleanly when human judgment is required. We build these with explicit checkpoints — because autonomous doesn't mean unsupervised, and the systems that earn long-term trust are the ones that know their own limits.

How we think about models and infrastructure

We're not loyal to any particular model or provider. We route based on what the task actually requires — frontier models for planning, reasoning, and orchestration; cost-effective open-source models for well-scoped, repeatable tasks where the output is predictable. We use self-hosted open source models where data sensitivity or cost profile makes that the right call. The model is a component. The architecture around it is what determines whether the system holds up.

What you won't get from us

A roadmap built around the tool we happen to be excited about this quarter. A recommendation to adopt AI because your competitors are. A system that looks impressive in a demo and requires a ML engineer to maintain. We have no interest in any of that. What we will do is make sure whatever we build earns its place in your stack — and keeps earning it.