Every engagement is scoped, practical, and built to last.

1. AI Readiness & Roadmap Sprint

What it is

Most businesses arrive at AI feeling two things at once: they know they're missing out, and they're not sure what they're missing out on. This engagement cuts through that ambiguity. We spend dedicated time inside your business — reviewing your tools, your data, your processes, and the places where friction quietly costs you — and we come back with a clear picture of where AI can help, where it genuinely can't, and what to do first.

The output is a written roadmap you can actually execute. Not a deck full of possibilities, but a sequenced plan that respects your team's capacity, your existing systems, and your risk tolerance. You leave with a shared understanding across stakeholders, a ranked list of opportunities, and the confidence to take a real first step.

What you walk away with

• Current-state process map highlighting manual bottlenecks, data flows, and tool dependencies
• AI opportunity inventory: scored by impact, feasibility, and readiness (data, tooling, team)
• Risk and governance notes for each opportunity (compliance flags, data sensitivity, human-oversight requirements)
• A sequenced 90-day roadmap with recommended starting projects
• Vendor and tooling shortlist aligned to your existing stack
• Executive summary for internal alignment and stakeholder communication

Who it's for

Founders, CEOs, COOs, or heads of operations who know AI should be on the agenda but haven't found the time or clarity to act on it yet. Also valuable for teams who have run a few disconnected AI experiments and want to consolidate them into a coherent direction before spending more. If you're pre-roadmap, this is where you start.

Timeline

Typical duration: 3–4 weeks.

2. Workflow & Agent Launch

What it is

Once you know what to build, this engagement builds it. A Workflow & Agent Launch takes one or two well-defined operational processes and automates them end-to-end using AI-powered agents, integrations, and logic — deployed into your actual environment and handed off to your team to run.

The scope is deliberately contained: one or two workflows, fully built, fully documented, and fully understood by the people who'll own them. We design the architecture, handle the integrations, write the prompts and logic, test for edge cases, and stay alongside you through deployment until it's running reliably. The goal isn't to impress you with what's possible — it's to deliver something your team trusts enough to leave running unattended.

What you walk away with

• Deployed workflow or agent(s) running in your live environment
• Full technical documentation: architecture, prompts, integration specs, data flows
• Testing and quality-assurance record (edge cases, failure modes, fallback behavior)
• Runbook for your team — how to monitor, maintain, and escalate when something unexpected happens
• Brief training session for the team members who own the workflow day-to-day
• 30-day post-launch check-in to catch any early issues

Who it's for

Heads of operations, department leads, and technical co-founders who have a specific workflow they want automated and the internal buy-in to deploy it. Works best when you have a process that's already well-understood — we're adding automation, not solving a process design problem from scratch. If your process is still messy, an AI Readiness & Roadmap Sprint is the better starting point.

Timeline

Typical duration: 4–8 weeks, depending on workflow complexity and the number of system integrations required.

3. AI Product & CX Pilot

What it is

For businesses that want to bring AI capabilities to their customers or end-users — not just to internal operations — this engagement designs and launches a contained AI-powered product feature or customer-experience enhancement. Think: an AI-assisted onboarding flow, an intelligent support deflection layer, a document Q&A for members, or a guided intake experience that routes customers more accurately.

This is a pilot by design. We scope it tightly, build it carefully, measure it honestly, and produce a clear recommendation on whether and how to expand. You end up with a working feature, real usage data, and a genuine view into what your customers respond to — rather than a long and expensive build that doesn't ship.

What you walk away with

• Deployed CX or product feature running in a real user environment (even if limited rollout)
• UX documentation: conversation flows, interface specs, fallback handling
• Evaluation framework: how success is defined and measured for this feature
• Results summary from the pilot period: engagement, satisfaction signals, error patterns
• Recommendation memo: expand, iterate, pause, or sunset — with reasoning
• Handoff documentation for your internal team or product/engineering owners

Who it's for

Founders, product leads, heads of customer experience, and operations leaders who want to test a customer-facing AI enhancement without committing to a full product build. Also valuable for companies whose current support or onboarding processes are high-touch and expensive, and who want data before investing in a larger solution. Requires an existing customer-facing surface (a product, a portal, a support channel) to integrate into.

Timeline

Typical duration: 6–10 weeks, including scoping, build, pilot period, and evaluation.

4. AI Ops & Improvement Retainer

What it is

AI systems are not fire-and-forget. Models drift. Usage patterns change. Edge cases accumulate. A workflow that ran cleanly in month one can quietly degrade over time if no one is watching. The AI Ops & Improvement Retainer is structured oversight for teams who have deployed AI (with us or elsewhere) and want confidence that it's still working the way it should.

Each month includes a set of defined activities: performance monitoring, drift checks, prompt and logic reviews, issue triage, and a prioritized improvement queue. We produce a short monthly report that tells you what the system is doing well, what's slipped, and what we're adjusting. We also stay available for urgent issues, new integration needs, or requests to extend an existing workflow. It's a relationship built for the long run.

What you walk away with

• Monthly performance report: accuracy, throughput, anomalies, user feedback patterns
• Drift and regression detection with documented findings
• Prompt, logic, and integration updates as needed
• Prioritized improvement backlog with recommendations
• On-call support channel for urgent questions or incidents
• Quarterly review: larger retrospective on what's working, what's not, and what to build next

Who it's for

Operations leads, technical founders, and heads of product or CX who have working AI systems and don't want to babysit them — but also can't afford for them to fail silently. Also valuable for teams whose in-house technical staff can manage day-to-day maintenance but lack the time or expertise for systematic review and optimization. Minimum commitment is typically three months.

Timeline

Ongoing monthly retainer. Minimum engagement: 3 months.

Frequently Asked Questions

How fixed is “fixed-scope” in practice?

We start every engagement with a written scope document that defines what we're building, what we're not building, and what counts as done. For sprints, scope is firm by design — the value comes from focus, and expanding mid-engagement usually just delays the outcome. For the Workflow & Agent Launch and CX Pilot, we have a defined change-request process: if something meaningful shifts, we document it, agree on impact, and decide together before moving forward. We won't invoice you for every small clarification, and we won't silently absorb scope creep either. Honest accounting, in writing.

What if our data isn't clean or well-organized?

Most of our clients don't have perfectly clean data — that's part of what we assess. In the Readiness & Roadmap Sprint, data quality and availability are explicit evaluation criteria, and we'll tell you honestly what shape your data is in and what that means for specific AI opportunities. For build engagements, we scope based on the data we actually have access to, not the data we wish existed. In some cases, a light data-preparation phase becomes part of the project. We won't build something on a shaky data foundation and cross our fingers.

Do you work with our existing tools and platforms?

Yes. We don't sell a proprietary platform, and we don't have preferred-vendor arrangements that influence what we recommend. Our job is to build within your existing environment wherever possible — your CRM, your helpdesk, your ERP, your cloud provider. When we recommend bringing in a new tool, we'll explain why and give you alternatives. We document all integrations so your team can maintain them without us.

Who owns the IP from the work?

You do. All code, prompts, documentation, workflows, and systems built during an engagement are yours. We retain no licensing rights and no ongoing access to your data or systems after an engagement ends. We'll ask your permission to reference the engagement type (not your name or details) in general descriptions of our work.

What happens after a sprint or launch engagement ends — can we still reach you?

After a sprint ends, you have everything in writing and can move forward independently. We're happy to answer follow-up questions over email for 30 days at no charge. For ongoing needs — maintenance, new features, system improvements — the AI Ops & Improvement Retainer is the right vehicle. For one-off requests between engagements, we can often accommodate them on a short-term basis; reach out and we'll tell you honestly whether we have capacity.

How do we measure whether this actually worked?

We define success metrics before we start, not after. In every engagement, part of the early work is agreeing on what “better” looks like — in concrete, measurable terms. For operational workflows, that might be processing time, error rate, or headcount hours reclaimed. For CX features, it might be deflection rate, satisfaction scores, or time-to-resolution. For the roadmap sprint, it's the quality and clarity of the output itself. We track these throughout and report on them honestly, including when we've fallen short of a target.