AI automation & implementation services
Automate the work eating your team's week.
AI running in production against your live data — classifying, extracting, drafting, triggering the next step. Your team supervises instead of typing.
Free 30-minute call. You leave with a clear plan, whether you hire us or not.
Fixed price · Fixed timeline · 12–24 weeks to launch
Every run logged. Human checkpoint on low-confidence rows. This is what shipping looks like.
Trusted by teams at
The hard part
Most AI projects never leave the pilot.
The demo is the easy part. Nearly nine in ten organizations are using AI somewhere, and roughly two thirds have never scaled it past a pilot. The gap is not intelligence. It is everything underneath the model.
The demo runs on clean sample data and falls apart on the real thing
Nothing is connected to the systems where the work actually happens
Output nobody trusts enough to remove the human review step
Security, PII, and access questions surface late and stop the whole thing
It was built by someone whose actual job is not maintaining AI systems
What usually happens
A pilot that proves AI could work here, and no path from there to something the business depends on.
What we build
AI running in production against live data, integrated with your systems, with governance that clears review.
The model was never the hard part. Everything around it is.
What you get
AI your team stops double-checking.
Three things separate AI that ships from AI that stalls. We build all three in from the start.
Rigor
Clean, structured, permissioned data underneath the model. Most failures are data failures wearing an AI costume.
Reliability
Workflows that run on schedule with error handling and fallbacks. Output your team can act on without checking every row.
Governance
Access control, PII handling, and audit trails designed in from day one. The question that stops most AI projects late, answered up front.
What we build
Production AI, built into the systems you already run.
Every engagement produces working automation, custom to your data and your workflow — data architecture, agent logic, integrations, interfaces, and the governance around them.
AI workflows and agents
Multi-step pipelines connecting your tools to GPT and Claude. Classify records, extract data, draft responses, trigger the next action. Running on a schedule, not on demand.
System integrations
AI connected to HubSpot, Salesforce, Slack, Airtable, Make, and custom APIs, so it acts on live business data instead of a spreadsheet export.
Data architecture
The unglamorous layer that decides whether any of this works. Structured, clean, and permissioned, because a model reasoning over messy data produces confident nonsense.
Dashboards and interfaces
Where your team sees what the AI did, approves what needs approving, and catches what needs catching. Automation people can supervise.
How it works
Our AI implementation process.
Most projects run twelve to twenty-four weeks from audit to handoff. You know what is being built and when, before anything starts.
We map what's actually there
Your data, your systems, and the real constraints. You get a straight read on what's feasible before anyone writes code.
Feasibility readA plan you can veto
Data structure, agent logic, and the human checkpoints, written down and priced. You approve it before it gets built.
Priced blueprintTested on your real data
Workflows, integrations, interfaces, error handling, governance. Run against production data, not a clean sample.
Working automationYour team supervises it
Trained on what it does, where it stops, and how to catch a bad run. Documentation written. You own it.
Docs + trainingWho this is for
The mandate is clear. The path to production isn't.
We work with operations teams, marketing teams, and mission-driven organizations. Different teams get stuck in different places.
“Leadership wants AI and we have no one to build it.”
The mandate is real, the timeline is real, and there is no internal capability to point at it.
“Our pilot impressed everyone and then went nowhere.”
You proved it could work. There is no path from that demo to something the team depends on daily.
“Security and legal block anything near our data.”
Governance, PII, and access control are the blocker, and nobody owns the answer.
Proof
Results our clients report.
Leo · AI discovery engine
Brilliant discovery calls. Mountains of unstructured data. Hours lost turning one into the other.
A transformation consultancy needed discovery calls to become structured proposals. Over three months we built Leo — it listens to client calls, identifies problems, and maps them to the firm's solutions. The pivotal piece wasn't the AI: it was formalizing their Delivery Framework into a solution-mapping matrix that became Leo's intelligence foundation.
Airtable · OpenAI · Fireflies · Make · Steamship
Read the case study →Who you work with
Mecca Parker
- Founder, Park West Digital
- Carnegie Mellon engineer
- Accenture consultant
- Fortune 500 clients
Fifteen years building operational systems — for Kraft Heinz, Disney, Kaiser Permanente, and national nonprofits with real compliance obligations. Getting AI into production is an operations problem: data architecture, systems integration, governance. That is the work we were already doing before it had this name.
Questions
What buyers ask before they book.
If yours isn't here, the consultation is the fastest way to get a straight answer.
What does an AI automation consultant do?
An AI automation consultant designs and builds AI into your actual operations: data architecture, agent logic, system integrations, and the interfaces and governance around them. Leo is one built end to end. The work is getting AI from a working demo to something that runs in production reliably.
How do we know this will not be another project that goes nowhere?
Because the plan is approved before anything is built, the timeline is fixed, and the build is tested against your real data rather than a sample. Most stalled projects fail on data quality, integration, or governance — all three are designed in at the start, not discovered at the end.
What can we show leadership in the first month?
The audit and architecture are complete inside the first six weeks, so you have a documented plan, a scope, and a timeline to report against before the build begins.
How much does an AI implementation cost?
Every implementation is scoped to the work. After a free consultation we give you a fixed price and a fixed timeline, so there is no ambiguity before you commit.
How long does an AI implementation take?
Most projects run twelve to twenty-four weeks from audit to handoff, depending on the number of integrations and the state of the underlying data.
How do you handle data security?
Access control, PII handling, and audit trails are designed in from the start, not added after. We have run formal security and data governance assessments for national organizations with real compliance obligations.
What AI tools do you work with?
We build on OpenAI and Claude, orchestrated through Make, n8n, and custom code, connected to Airtable, HubSpot, Salesforce, Slack, and custom APIs. Tool choice follows the problem.
Do you build custom AI agents?
Yes. Multi-step agents that classify, extract, draft, and trigger downstream actions, with human checkpoints where judgment is required.
Next step
Show us the work you'd hand off first.
Book a free 30-minute consultation. We look at your data and systems, tell you what is realistic on what timeline, and are honest about whether we are the right fit.