Tiny Agentic Martians — AI Integration & Solutions
Agentic AI, Integrated Into How You Work
Autonomous AI systems that take over the workflows your team does on repeat. Reporting, data entry, document processing. No one gets replaced. Everyone gets their time back.
“Agentic workflows are the biggest game changer we've seen in years. Harnessing them without rewriting how we work was the only way to make it happen.”
— Justin Lee, Founder
What Changes When AI Handles the Repetitive Work
Weekly Reporting
Before
Days of manual work
After
Automated, ready in minutes
Document Processing
Before
Bottlenecked by manual effort
After
Scales past manual limits
Compliance Checks
Before
Periodic and reactive
After
Continuous and real-time
Data Entry
Before
Hours every day
After
Minutes of review
Lead Scoring
Before
Manual review
After
Scored in real time
Illustrative of what TAM designs these systems to do for a team like yours, not results from past clients.
How AI Takes Work Off Your Plate
Every engagement starts with one of these.
Custom AI Applications
Off-the-shelf AI doesn't know your business. TAM builds custom tools that connect to your data and run inside your infrastructure, so the software adapts to how your team works. Not the other way around.
Agentic Workflow Automation
The emails, CRM updates, reports, and exception handling your team does on repeat. Turned into pipelines that run themselves on your schedule. No babysitting required.
AI Strategy & Integration
Not sure where AI fits? A full audit of your current stack, the workflows burning the most hours identified, and a phased rollout plan ranked by ROI. Start with the win that funds the next one.
Ongoing AI Operations & Support
Systems that run unattended still need an owner. Prompt tuning, model upgrades, and someone to call when output slips, handled on retainer. The system that saved you 40 hours a week keeps saving you 40 hours a week.
Why Most AI Projects Stall
Four ways these projects die. Every one of them is a design decision, not bad luck.
The pilot works, production doesn't
Demos run on clean sample data. Real data is messy, half the edge cases are undocumented, and nobody built the path for what happens when the model isn't sure. Systems that can't handle exceptions don't survive contact with production.
Nobody owns it after launch
Models update and change, prompts go stale, or APIs change and break your builds. Most projects ship without monitoring and degrade quietly until someone notices the numbers are wrong. By then the team has stopped trusting the output.
It automates the wrong thing
Teams automate what's easiest to automate, not what's most expensive to keep doing. You end up with a working system that saves four hours a week while the forty-hour problem sits untouched.
The team never trusts it
A black box earns no trust. When your team can't see how the AI reached a decision, they keep shadowing it with manual work, and the hours you meant to save never show up.
Catching these early is what the first two weeks of any engagement are for.
Discuss Your Use CaseSystems That Survive Production
Agent systems with review built in at every layer, not one-off scripts that break the first time your process changes.
Architected
Most of the value is in designing the system and reviewing what the agents produce. That's where quality and speed come from, and greater impact comes from scaling agent compute.
Exceptions are the design
The interesting work isn't the 90% an agent handles cleanly. Rather it's the 10% it shouldn't. Every pipeline gets an explicit path for low-confidence cases, and a person on the other end of it.
Monitoring ships with the build
Every system ships instrumented from day one. You can see how it's performing without waiting for a customer to tell you something broke.
Agents show their work
A system your team can't inspect is one they'll quietly redo by hand. Every decision stays visible, so people can spot-check the output instead of starting over.
You're buying a system that holds up, not hours of bespoke development. That's how output grows, by taking the repetitive work off your team's plate.
How an Engagement Works
Four phases. Measurable milestones at each one. Most teams are live within 8 weeks.
Discovery & Assessment
Your workflows, data sources, and team structure mapped. The automation opportunities that pay for themselves fastest identified and prioritized.
Architecture & Strategy
Models selected, pipelines defined, architecture designed. A phased roadmap with clear milestones lands before a single line of code ships. No surprises. No scope creep.
Build & Integrate
Built in 2-week sprints, shipped to staging first, integrated with your existing tools via APIs and webhooks. Working software visible every two weeks, not a big reveal at the end.
Operate & Optimize
The system moves into ongoing ownership. As your data and processes shift, it gets adjusted to match, so it keeps working instead of slowly falling out of step with the business.
Who We Serve
If your team loses hours every week to repetitive work, you're in the right place. The biggest wins come from the work closest to your revenue, so that is where TAM starts.
Growing teams
Best fit for companies of roughly 50 to 500 people, where the volume of manual work makes automation pay off quickly.
Proven in your world
Most AI vendors need your business explained to them. TAM comes from a career on the revenue side of SaaS, with the engineering background to build the fix.
Start small or scale up
Start with one workflow. When it pays off, expand to a full system on the same foundation.
Anyone ready to start
No technical background or finished plan required. If you have a workflow worth automating, that is the starting point.
Revenue Operator. Engineer. AI Builder.
TAM was founded by a revenue operator who builds. The gap between AI demos and AI running in production is where most projects die. TAM exists to close it.
Builds, Doesn't Just Advise
A startup and freelance engineering background. What ships is working software.
Revenue DNA
Every system starts from the metric it has to move and improve.
Cross-Functional Fluency
Fluent in engineering, product, sales, and operations.
Built for Outcomes
A Founding Cohort, Hands-On
TAM is taking a small founding cohort each quarter, with direct founder involvement and real client input on what gets built next.
Direct founder access
Justin Lee leads every engagement personally. No handoff to a junior team.
A small cohort by design
Up to three clients at a time. Quality and depth over volume.
Shape what gets built
Founding clients help define how TAM works. Your priorities set what comes next.
Security & Data
How TAM handles your data, IP, and access.
Your data, your ownership
You own your data. TAM owns the platform. Code and IP for systems built under each engagement is defined in your contract.
Hosted on AWS and Vercel
Sub-processors and regions are detailed in the Privacy Policy.
Deletion on request
On termination or written request, all customer data is deleted within 30 days, including backups.
Founder-only access
Access to customer data is restricted to the founder. Infrastructure providers (AWS, OpenAI, Anthropic per integration) are sub-processors under their respective security policies.
Let's Map Out Where AI Fits
Start with a quick strategy call. 30 minutes, zero obligation. You'll leave knowing which workflow to automate first, whether or not it leads to an engagement.
Up to three clients at a time