Martin Tech Labs redesigns high-friction workflows around people, AI agents and the systems you already use, then measures the result in hours recovered, quality, throughput and cost per outcome.
Baseline first. Governed from day one. Built around the people you already have.
What does your repetitive work cost?
People doing this work
6
Hours a week each spends on repetitive work
8 h
Copying data between systems, checking and matching records, routing requests, pulling facts out of documents, chasing missing information.
Average cost per person, per year
$90,000
Salary plus benefits.
Cost today $108,000 a year. Value of the time you could get back, up to $86,400 a year.
Estimate, not a guarantee
Excludes AI tool and build costs. Assumes people still review 20% of the repetitive work, and a year has 1,800 working hours.
You're already paying for AI. People use ChatGPT, Claude, Copilot and specialized tools. Teams run pilots. Someone has built an internal app. Leadership still can't answer one question:
“What are we actually getting for the money?”
Licenses, tokens and pilots keep growing. People say AI helps. Nobody can say by how much, or which pilots to scale and which to stop.
The same people still re-key data, bridge systems by hand, check every output and dig through inboxes to find what matters.
Overlapping copilots, point tools and internal apps. Duplicate spend, scattered data, and tools nobody owns or maintains.
What can AI see? What can it do without asking? Who maintains it when the vendor or model changes? Uncertainty stalls the rollout.
The opportunity is rarely glamorous. It sits in five kinds of recurring work that eat expensive attention every day.
Today
Someone reads an invoice, contract or intake form, pulls out the fields, and types them into another system.
Redesigned
An agent extracts and validates the fields, writes them back, and sends only the unclear cases to a person.
Measured by: Hours recovered, error rate, throughput
Today
Export from system A, clean it in a spreadsheet, import into system B, then message someone that it's done.
Redesigned
An agent moves the data between systems, keeps state in sync, and posts the update itself.
Measured by: Manual touches, cycle time, completion rate
Today
A senior person re-checks every record for correctness, applies the rules by memory, and fixes what's wrong.
Redesigned
Automated first-pass checks with confidence thresholds. People review the exceptions, not the whole pile.
Measured by: Errors per unit, rework, exceptions
Today
Cases that don't fit the rules sit in a queue or someone's inbox until a person notices, works out who owns them and forwards them.
Redesigned
Each exception arrives classified, with context gathered, at the person who owns that decision.
Measured by: Time in queue, exceptions per 100 cases, escalation accuracy
Today
Email, Slack, tickets, transcripts and dashboards. People read everything to find the few items that matter.
Redesigned
Agents monitor the streams, resolve routine items, and surface only what needs a decision.
Measured by: Attention hours recovered, response time, missed items
We are not paid to create AI activity. We are paid to create operating capacity that compounds.
Nothing is built before it's measured. Nothing goes live without controls. Each deployment makes the organization better at deploying the next one.
01
Find the workflows consuming time, creating errors, delaying decisions or limiting throughput. Establish the baseline before anything changes.
Hours, human touches, rework and error rate, cycle time, throughput, cost per outcome.
02
Redesign the workflow around people, deterministic automation, AI agents, systems, data, permissions, validation and escalation.
Humans keep judgment and accountability wherever consequences or ambiguity rise.
03
Build into production, integrate with your actual stack, add validation, controls and observability, train operators and instrument the workflow.
Nothing goes live without approval points, logging, stop conditions and a way back.
04
Measure results, improve what works, capture reusable agent patterns, decision rules and integration components, and apply them to the next workflow.
Each deployment should make the organization better at deploying the next one.
The goal isn't maximum automation. It's better operating economics.
AI is not a layoff strategy. We start with the parts of the job that should never have required expensive human attention in the first place.
Every example on this site carries a label, so you always know what you're looking at. We publish client results only once they are measured against a baseline and the client agrees to share them. Until then, our examples are clearly marked reference architectures.
Running in production for a customer or for Martin Tech Labs internally.
Baseline, intervention, timeline and a measured result, shared with permission.
A working build that shows capability. Not a client production deployment.
An illustrative design showing how we would approach the workflow. Not a client result.
Level 1
Did we recover human capacity?
Hours recovered per month, time per case, waiting time
Level 2
Did the work get more accurate?
Errors, exceptions and rework per 100 cases
Level 3
Can the same team complete more?
Cases processed, contracts reviewed, customers onboarded, tickets resolved
Level 4
What does each outcome cost now?
Cost per completed outcome, output per FTE, capacity freed for growth
Level 5
Did it move the P&L?
Operating cost, margin, revenue
Find the workflows where AI can create measurable operating leverage, and calculate the economics before you build anything.
COOs, heads of operations and transformation leaders whose company already spends on AI but can't yet prove what it's producing.
Your CFO, CIO or CTO and workflow owners are welcome in the room. The audit is built to answer their questions too.
If the audit finds workflows worth building, implementation deploys them with your team and measures them against the baseline. If it doesn't, you keep the map and the do-not-build list.
If we're not the right firm for the problem, we'll say so.
Beyond the hype. Build capacity that compounds. Our publication for operators, alongside practical decision tools.
Practical AI for operators building more output, better decisions and measurable operating leverage from the teams they already have.
Workflow teardowns, AI economics and implementation decisions for operating leaders.
Start with the Workflow Economics Calculator: put a number on one workflow before anyone builds anything.
Martin Tech Labs sits between strategy-only AI consulting and task-level automation agencies. We start with the economics of the operation, redesign the workflow, deploy it into production and measure what changed.
The hard part of operational AI isn't the demo. It's integration, controls, maintenance and proving the result. That's the part we're built for.
About Martin Tech LabsStraight answers to what we hear most. If yours isn't here, bring it to Assess a Workflow.
Bring us one workflow that is slow, repetitive, error-prone or expensive. We'll help determine whether AI is actually the right lever.
