I build AI agents and ML for one of two reasons: get you more customers, or kill the work you're overpaying for. I prove it with a quick MVP — and you only pay when it works.
Agents that find, win, and keep customers — outreach, follow-up, content, support that never sleeps.
Kill the repetitive work you're overpaying people to do. One agent, a fraction of the headcount.
Forecasting, scoring, computer vision — a real trained model when a prompt could never do the job.
Turn your contracts, SOPs, and docs into instant, cited answers your team can actually trust.
Legally-sourced data pipelines that feed your sales, research, and models — built to stay up.
Two outcomes, four ways to get there. No buzzwords — the concrete work.
Agents that do the work you'd hire three people for — data entry, follow-ups, triage, reconciliation. You keep the judgment; the machine does the grind.
→ an AP agent that 3-way-matches invoices and escalates only real exceptions.
Lead research, personalised outreach, relentless follow-up — the top-of-funnel work that fills pipeline without hiring a sales team.
→ an outreach agent that researches a prospect and drafts a genuinely personal email.
Forecast demand, churn, fraud, and equipment failure — act before it costs you, instead of paying to clean up after.
→ a model that flags the customers about to churn while you can still save them.
Connect your messy spreadsheets, databases, and tools → automated analysis → clean reports your team reads.
→ the weekly report that took an analyst a day, generated in minutes — with the 'why'.
Every industry has its own expensive, manual bottleneck. Here's where I've built — and what for.
Not a black box. Here's the exact path every Opsroad agent runs — scroll through it.
You tell me the bottleneck and connect the tools it touches — inbox, CRM, database, docs. The agent maps exactly what data it can read and what actions it's allowed to take. Nothing more.
Before it acts, it pulls live ground truth — web search, your documents, your systems — so every decision is based on reality, not a guess from training data. This is where most 'AI' quietly skips a step.
The agent decomposes the task, decides which tools to call in which order, and checkpoints its state at each step — so a failure halfway through resumes instead of restarting from zero.
It executes through your connected accounts — sends the email, files the claim, updates the record, generates the report — inside hard guardrails you set. Real actions, not just suggestions.
Each output is scored against evals before it's trusted. Confident, correct results ship automatically; anything ambiguous routes to a human. The agent never pretends it's sure when it isn't.
The job is done — and you see a full, auditable trace of every step, tool call, and decision. Observable, repeatable, and yours to inspect at any time.
Most agents work once on stage and break in production. Every system I ship runs on the same layered architecture — observable, recoverable, private from day one.
Every workflow checkpoints state and retries on failure — it survives the network blip your demo never tested.
Traces, evals, and logs are wired before feature one. You see exactly why it did what it did.
Runs on your cloud or fully on-prem. Your data — customers, contracts, claims — never leaves your walls.
The reason serious teams won't touch most AI tools: their data leaks to a provider. Here's the exact path that stops it — the model only ever sees redacted tokens, and the dictionary that could re-identify anyone never leaves your walls.
<PERSON_1>MRN 4471 → <ID_1>aarav@… → <EMAIL_1>↑ reversible dictionary — stays inside your VPCThe model only ever sees <PERSON_1> — never the patient. The mapping that could re-identify them never leaves your environment, and is reversed only after the model has answered.
Before a single character reaches a model, a redaction gateway (Microsoft Presidio — NER + regex + checksums) swaps every name, ID, and record number for a placeholder like <PERSON_1>. A reversible dictionary, held in your VPC, restores them only after the response. The model never sees a real value.
Every client runs behind its own virtual key on a LiteLLM gateway: a scoped model allow-list, a per-key budget, routing, fallback, and a full audit log. No client's traffic, spend, or context ever touches another's.
Self-hosted (vLLM / SGLang) on your hardware, a private VPC endpoint (Azure OpenAI / Bedrock — the vendor is never on the data path), or a zero-data-retention enterprise API. The most sensitive workloads never touch a public provider.
Your documents live in a vector DB you control; the model stays static and is never trained on your data — the 2026 gold standard for keeping IP yours.
Only the minimum field a step actually needs is ever sent. A living inventory classifies PII, PHI, and financial data by owner and retention policy.
Every call logs the entities detected and confirms masking was applied — the evidence layer HIPAA, SOC 2, GDPR, and India's DPDP auditors expect.
Representative builds — the real kinds of system we ship, by problem. Clients are anonymised and the figures are illustrative, not a verified scorecard.
No retainer, no agency markup, no six-month timeline. The risk is on me.
One call to find the slow, manual, or expensive thing — and whether AI is even the right answer.
A working version of the fix, fast — so you can see it do the job before spending real money.
Happy with the MVP? We turn it into the production build. If not, you owe nothing.
Every engagement starts with a pilot. If it doesn't do the job we agreed, you owe nothing — and you keep everything we learned about your problem.
Then you don't pay. Every engagement starts with a pilot MVP — if it doesn't do the job, you owe nothing and keep the learnings.
No. Keys are used per-session and never stored. Models can run private or fully on-prem, and your data is never used to train shared models.
Usually 2–3 weeks from a clearly-defined problem to a working pilot you can actually test on your own data.
The pilot is a small fixed fee. The production build is scoped only after you've seen the MVP work — no surprise invoices, no open-ended retainers required.
Yes. You describe the problem in plain language; I handle the models, tools, integrations, and infrastructure end to end.
No. Agents, custom ML, computer vision, RAG — I pick the right tool for the problem, which is often not a chatbot at all.
Checkpointing, evals, observability, and a human-in-the-loop on the ambiguous cases — the layered architecture above. Most 'AI' skips this, which is exactly why it dies in production.
I'm Meet. I started Opsroad because most AI work is theatre — decks and demos that never survive a real workload.
I'd rather show the work: build the thing, ship it, and only get paid when it delivers. No agency overhead, no juniors, no six-month timelines — you work directly with the person building it.
If you've got a process that's slow, manual, or expensive, tell me about it.
— Meet · founder, Opsroad
How production AI actually gets built. No fluff, no AI-generated filler.
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