Three ways to start.
One way to finish.
Every engagement ends with something running in production. Where you start depends on how clear the problem is — and how big you want to swing.
The Audit exists for the most common situation we see: leadership knows AI should be doing more in the business, but isn't sure where it pays off, what to build, or whether to build vs. buy.
In two weeks we map your workflows, identify the 3–5 highest-ROI agent candidates, and give you a ranked recommendation with rough scope, integration paths, and build-or-buy guidance for each.
What you get
- A workflow audit of 2–4 functions you nominate (sales, support, ops, etc.)
- A ranked map of 3–5 agent opportunities with estimated ROI for each
- Architecture sketches for the top 2 opportunities
- Build-or-buy guidance — including when an off-the-shelf tool is the right answer
- A kickoff scope ready to green-light if you want to move forward
- A 60-minute readout with your leadership team
How it runs
- Week 1: discovery interviews with the teams doing the work today
- Week 2: synthesis, ROI modeling, and the deliverable
- Roughly 4–6 hours of your team's time across the two weeks
What it isn't
- A McKinsey deck. The deliverable is short, specific, and operational.
- A sales pitch. About 40% of audits end with us recommending an off-the-shelf product, an internal hire, or doing nothing.
Agent Pods are our productized builds — specific agents for specific job functions. Sales triage, support copilot, document review, knowledge base, listing moderation, and more.
Each pod is built from a proven blueprint we've shipped before, customized to your stack and data. That's how we get to 4–8 week timelines without cutting corners on the engineering.
What you get
- A production-deployed agent running in your stack
- Integrations with your tools (CRM, helpdesk, data warehouse, etc.)
- An evaluation set tuned to your real data and edge cases
- Monitoring dashboards (latency, cost, accuracy, escalation rate)
- Team training and full documentation
- 30 days of post-launch tuning and bug fixes included
How it runs
- Week 1: discovery, data access, eval set definition
- Weeks 2–4: build & iterate against the eval set
- Weeks 4–6: shadow run with your team, refine
- Weeks 6–8: production launch, monitoring, handoff
The 18 pods we offer today
Browse the full library on the agents page — each one has its own timeline and example outcomes.
Custom Platforms are for when a Pod isn't enough — multi-agent systems, bespoke RAG architectures, custom UIs, complex integrations, or proprietary workflows that don't fit a template.
Every Custom Platform engagement starts with an Audit. We need a clear scope before either of us signs up for a multi-month build — and you need to know we're the right team before betting six figures on it.
What you get
- A bespoke multi-agent system or AI platform built around your business
- Custom UIs, internal tools, or end-customer-facing features
- Architecture documented for your engineering team
- Full eval and observability stack
- Knowledge transfer so your team can extend it
- 60 days of post-launch support
How it runs
- Always starts with a 2-week Audit (scoped separately, credited toward the build)
- Months 1–2: foundations, core agents, integrations
- Months 2–4: workflows, UIs, evals
- Months 4–6: shadow run, refinement, production launch
- Bi-weekly steering check-ins with your leadership
When this makes sense
- You have a workflow that's truly novel — no off-the-shelf agent maps to it
- You need multiple agents that coordinate
- You want a customer-facing AI surface that needs to be designed, not just engineered
- You're committed to AI as core infrastructure, not just an experiment
AI agents aren't ship-and-forget. Models change. Data drifts. Your business changes. A retainer keeps the system tuned and improving.
Most clients who ship a Pod or Platform with us move into a retainer after the 30/60-day included support window ends. About a third don't — and that's fine.
What's included
- Ongoing eval monitoring — we catch quality regressions before your team does
- Cost and performance optimization (often pays for itself)
- Model upgrades when better ones ship
- Small feature additions and tuning based on user feedback
- Monthly report on what changed, what's working, what to improve
What buyers always ask.
It depends on the architecture. For most builds we use API calls to enterprise model providers (Anthropic, OpenAI, Azure) with zero data retention enabled and SOC 2 / HIPAA-compliant deployments. For more sensitive cases we can deploy fully on your infrastructure using open-weight models. We make the call together during the Audit or scoping conversation.
You do. All code, prompts, eval sets, and documentation produced during the engagement transfer to you on delivery. We retain the right to reuse generic patterns and our internal tooling — not your business logic, prompts, or data.
The Audit is the safety mechanism. If after the Audit we don't think a build will hit the ROI you need, we'll tell you and you walk away having only paid the $5K–$10K. For builds, we agree on success metrics in the kickoff scope — measured against the eval set — and we don't call it done until we hit them.
Yes — and most of our clients do, eventually. We build with handoff in mind: documented code, runbooks, eval frameworks, and team training. The retainer is optional. We'd rather have you happy and self-sufficient than locked in and resentful.
We're engineers first — that's the whole point. We work with whatever you have: REST APIs, GraphQL, SOAP, databases, on-prem systems, custom auth, weird legacy stuff. The Audit confirms feasibility before either of us commits to a timeline.
Third-party costs (model API tokens, vector DB hosting, infrastructure) are passed through at cost. Major scope changes mid-engagement are handled via a change order. We don't do generic strategy consulting, AI training programs, or build things we don't think you should build.
We build and ship. Most AI consultancies stop at the deck. We stop when there's a working agent in your production stack that's tested, monitored, and accepted by the team that has to live with it. The 12+ years of production software engineering is what makes this possible — it's not the LLM that's hard, it's everything around it.
Yes. Mutual NDA is standard before the Audit. We also have a one-pager on our security practices, ZDR setups, and data handling we can share alongside it.
Still not sure
where to start?
That's exactly what the Audit is for. Two weeks, fixed scope, no commitment beyond it. Worst case you get a ranked map of your AI opportunities.
Book the audit →