I find the work AI can do for you — and I build it.
I'm Joshil Jain, an AI consultant with OffPattern Labs. Most businesses don't need a brain. They need the boring, scheduled, dollar-measurable job handled properly. That's what I build — running, monitored, and attached to a number.
joshil@offpatternlabs.com
Six things I get paid to build
Each one starts with a number the business already cares about. If I can't name the number, it isn't the right project.
Agents that run on a schedule
Not prompts you paste. Workflows that fire whether or not anyone remembers — pulling live data, filing the output, and flagging what needs a human decision.
- Reads from the systems where the data actually lives
- Writes back, not just advises
- Fails loudly instead of guessing
Browser & web automation
Legacy platforms with no API, gated portals, dynamic dashboards — the work nobody wants to do by hand, every single morning.
- Structured output, not a paragraph you have to parse
- Human-in-the-loop for CAPTCHAs, 2FA, and judgment calls
- Circuit breakers so a bad run stops instead of spiralling
Research that comes back sourced
Market, competitor, and supplier research where every claim traces to a document you can open. If it isn't in the record, it gets flagged as unproven.
- No invented market sizes or unattributed statistics
- Sources attached to the finding, not listed in a footnote
- Written as a deliverable, ready to hand to a client
Data reconciliation
The unglamorous work — matching what should have happened against what did. Settlement lines, fees, inventory, shipments.
- Line-level, across full reporting periods
- Surfaces the silent errors that compound monthly
- Ends in a recoverable number
Decision support before you spend
A cheap pre-flight check on a decision you're about to make with real money — positioning, pricing, copy, and go-to-market.
- Runs in hours, not the weeks a research panel takes
- Produces a verdict and a scorecard, not opinions
- Deliberately says what the method cannot tell you
Content and video pipelines
Production workflows for teams producing at volume, using your own footage and assets rather than stock filler.
- Built around your existing library
- Repeatable batch runs, not one-off edits
- Consistent output across every asset
Four rules that make AI output worth paying for
Anyone can make a model produce a paragraph. These are the constraints that turn it into something a business can act on — and that a competitor can't just copy a prompt to get.
Structured output, always
Free-form prose can't be ranked, diffed, or put in a deliverable. Every output I build lands in fixed fields — a verdict, a score, a confidence, a next action. That's what makes it comparable between runs and reusable by your team.
Grounded in the record
Every claim traces to a source you can open. Where the evidence is thin, I mark it as unproven instead of inventing a plausible number. A finding you can't source isn't a finding — it's a guess with formatting.
No flattering the wrong answer
The default posture of most AI is agreement. I invert it: the starting answer is no, the model has to earn the yes, and it must name what would stop a real buyer from proceeding. I test that before shipping it.
Run it twice, then diff
One run proves nothing — models vary. If two runs pick different winners, the options were too close to call, and that instability is itself the finding. I only ship what holds up on a repeat.
Four steps, and the first one is the hard one
Most AI projects fail because nobody agreed what success was before the build started. That agreement is the work.
Name the number
We start with the metric that's already leaking — recovered dollars, hours returned, revenue protected. If we can't name it, we stop.
Go find the evidence
Real data, real reviews, real documents. Not assumptions dressed up as research. Where the record is thin, I tell you it's thin.
Run it live
I build it and run it on the actual environment — no mocked-up demos, no screenshots of a prototype. If it breaks, we see it break and fix it.
Hand it over
You get the working system, the written diagnosis, and the instructions. Documented so it runs without me in the room.
Things I built and ran, not things I plan to build
Every item below was executed end-to-end on a live environment. The outputs exist — that's what separates this from a pitch.
Four workflows that run end-to-end, not in theory
Sourcing a deal and drafting the inquiry email across two tabs. Reconciling supplier price and stock into structured JSON. Turning public buyer chatter into a scored lead list. Triaging signups against an eight-row fraud table. Each one was executed live against the real site, and each one hands back a table instead of a paragraph.
A competitor teardown shipped as a real report
Sourced, monitored, and rendered to a five-page PDF — with the extraction pipeline, the change monitor, and the open-source toolkit that produced it. The point of the deliverable was never the PDF. It was proving the pipeline runs on a schedule without a human driving it.
Forty-four AI services mapped to buyer pain
Not organised by platform — organised by the six jobs a business already has and wants done: get the money back, stop the leak, find the growth, make the thing, know the numbers, serve the buyer. Each entry names the data it runs on and why a chat window can't do it alone.
A five-persona panel that argues
Synthetic buyer personas that debate each other, with a moderator keeping an objection ledger. Grounded in real review data, so every objection cites a line someone actually wrote — and anything unsupported gets flagged as unproven instead of being quietly invented.
The ones worth asking
Tell me the job nobody wants to do.
Send me the recurring task, the report nobody reads, or the portal that keeps breaking. I'll tell you what it would take to automate it — and if it isn't worth automating, I'll say that too.