Strategy, software, and enablement — in one engagement.
Working AI systems in production within weeks, integrated into the tools your team already uses, and handed off so your people know how to run them. One contract, one team, one outcome.
Who this is for
- Small and growing companies, 15–250 employees, no in-house AI team.
- Teams with a real workflow that could be faster, better, or cheaper with AI.
- Leaders who've been asked "what's our AI plan?" and want a grounded answer, not a 100-page deck.
Who this isn't for
- Pure research projects or pre-seed builds without a real workflow attached.
- Staff-augmentation only — we can extend an engagement, but bodies-for-hire isn't the shape.
- Big-4-scale transformation programs. Wrong firm; we ship systems, not roadmaps.
The capabilities we bring
One engagement, blended to the problem. Most engagements use two or three of these. Some use all four.
Advisory
Figure out where AI actually makes business sense — before you build anything.
Who it's for
- You've been asked "what's our AI plan?" and don't have a confident answer.
- A pilot stalled. You want an outside read before spending more.
- You're preparing to fund AI work and want someone to pressure-test the direction.
What you get
- A prioritized list of 3–7 candidate use cases with effort, impact, and risk.
- A recommended first system with a one-page architecture sketch.
- A buy/build/wait call on each candidate.
- A governance and risk note — data, models, compliance basics.
- A working session with leadership, not a presentation.
Custom development
Build the AI system. Ship it to production.
Who it's for
- You already know the use case and want it built well.
- Your first try at the same use case stalled or felt off.
- You want a working system, not a vendor evaluation.
What you get
- A production system, deployed in your environment or ours.
- Source code, documentation, architecture diagrams.
- Observability and evaluation hooks so you can see how it's performing.
- Human-in-the-loop checkpoints where appropriate.
- A reliability profile — known failure modes, kill switch, rollback plan.
- A handover period with your team.
Integrations & automations
Connect AI to the tools you already use.
Who it's for
- You have sticky tool stacks and manual hand-offs between them.
- You bought an AI tool that isn't plugging into your workflow.
- You want less human glue work between systems.
What you get
- Integrations that survive vendor API changes — versioned, monitored, tested.
- Automations with clear ownership, logging, and a documented runbook.
- A single map of how data and decisions flow across systems.
- Optional scheduled health checks and alerts.
Training, adoption & managed services
Your team runs it after we leave — and we're around if you need us.
Who it's for
- Your team just got a new AI system and needs to adopt it.
- Your people need to level up on AI tools, not because you bought one — because the world shifted.
- You want periodic outside check-ins instead of a full-time AI hire.
What you get
- Hands-on, role-based workshops on the tools your team actually uses.
- Office hours, champion programs, light-touch change management.
- Periodic system check-ins, model upgrades, eval reviews.
- A retainer bucket for incremental improvements and "we're thinking of trying X" conversations.
- A dedicated point of contact — not a ticket queue.
Pricing is per engagement, fixed-fee against scope. Walk us through the problem.
How engagements flow
One contract. One team. No hand-offs between strategy and build, or between build and your team.
- 1
Discovery
Conversations with the people who feel the pain. We look at the workflow, the tools, the data, and the team. Usually a week, sometimes two.
- 2
Scope
A one-page architecture sketch, a named first system, and a fixed-fee proposal. You see exactly what we'd build and why before signing.
- 3
Build
We ship to production in weeks, not quarters. Observability, evals, and human-in-the-loop checkpoints are built in on day one.
- 4
Handover
Runbook, observability dashboard, eval suite, role-based training. Your team can run, debug, and improve the system without us.
- 5
Optional retainer
Monthly hours for tuning, eval review, model upgrades, and "we want to add this" conversations. A partner, not a dependency.
Representative engagements
Shapes the work takes, pulled from real client situations. Full case studies are coming — we're writing them up from active work.
Problem
30-person legal services firm asking "where should we start with AI?"
What we built
3-week advisory engagement produced a prioritized list of 6 use cases, a first-system recommendation (intake triage with human review), and a one-page architecture sketch.
Outcome
They picked their first build engagement with confidence. The "don't do this yet" list was as load-bearing as the "do this first" list.
Problem
45-person services company needed AI-assisted ticket triage and response drafting.
What we built
8-week build pulling from Zendesk, knowledge base, and product docs. Routes tickets by intent, drafts first-pass replies for human review, logs every model call for audit.
Outcome
Agents review drafts instead of writing from scratch. Average handle time drops; quality stays flat or improves. The team tunes it themselves.
Problem
25-person services company had an AI summarization tool, a CRM, and a support tool — none of which talked to each other.
What we built
4-week integration. Call summaries land in the CRM. Tickets get auto-tagged by product area. One timeline across the three systems.
Outcome
Same people, same work. They stopped copying and pasting between five tabs.
Problem
40-person operations team just finished a build engagement with us and needed the system to stick.
What we built
4-week adoption block — 2 role-based workshops, runbook walkthrough, 3 weeks of office hours, documented champion. Followed by a monthly retainer at 12 hours/month.
Outcome
Nine months later the system is still running, has been extended twice, and the team hasn't called in a panic once.
The most common engagement shape right now: Workplace Agents.
Most active Services engagements today are deploying and configuring Workplace Agents — our productized managed AI employee for Slack. The work blends advisory (which channels, what each does), custom development (configuring personas and workflows), and training (the team learning to direct the agent).
If you want something useful in your workspace in the next few weeks and don't have a defined custom workflow to build yet, the Workplace Agent door is usually the faster first step.
Things you might be thinking
We get these a lot. Short versions here; deeper answers on a call.
We've heard this from other firms before.
Fair. We'd rather show you a working system in the first call than pitch from slides. Walk through the architecture of one we built, ask us what went wrong, and decide from there.
Aren't you just a small shop?
Yes — small enough to ship in weeks, deep enough to do it well. The same person in the kickoff is in the handover. We grew out of an AI training practice, which is why the third pillar of every engagement is your team running the system after we leave.
Our data is sensitive.
We default to your infrastructure, your model choice, your data residency. We don't move data we don't need to. Governance and observability are day-one, not phase two.
Will my team actually use it?
That's the question enablement answers. Every engagement ends with a handover period — runbook, evals, role-based training. We don't sign off until your team is running it without us.
Tell us what you're working on.
Even one sentence is fine. What's the workflow, what's stuck, what would good look like? We respond within one business day.