We build AI products for early-stage companies, put AI to work inside established ones, and train the teams that run it.
From idea to production, and measured after it ships.
What we do
1/3
Build it
Product ideation to production.
Technical feasibility · Rapid prototyping · Production engineering · Launch and handover
For founders and new products
2/3
Run it better
AI inside the work you already do.
Workflow discovery · AI opportunity assessment · Automation and copilots · Integration with what you already run
For companies of 10–200 people
3/3
Make it stick
Your team runs it without us.
Team training · Internal champions · Guardrails and governance · Ownership and handover
For every team we build for
We baseline the work before we build, and measure it after.
1/3 · SERVICE
Build the thing you can't yet build
Technical feasibility · Rapid prototyping · Production engineering · Launch and handover
You have a product idea and a clear sense of who needs it. What you don't have is the engineering to make it real — or the certainty that what you'd build would survive its first hundred users. Come work with us. We'll pressure-test the idea, put a working version in front of real people in weeks, and build the version you can put your name on.
2/3 · SERVICE
Get more out of the week you already have
Workflow discovery · AI opportunity assessment · Automation and copilots · Integration with what you already run
Quotes that take two days should take two hours. Invoices shouldn't be retyped. Monday's report shouldn't take until Tuesday. Come work with us. We'll find the two or three places AI genuinely moves the numbers, build them properly, and show you the before-and-after.
3/3 · SERVICE
Make the gain stick
Team training · Internal champions · Guardrails and governance · Ownership and handover
An efficiency gain nobody trusts lasts about a month. So we build the capability alongside the software: your team learns what the tools do well, where they fail, and how to tell the difference. The result is a system people actually use — which is the only kind that saves anything. Come work with us; this is the part most firms skip.
How we work
Observe
We start with where the hours go. What takes the time, what gets typed twice, what everyone waits on.
Prototype
The smallest working version in real hands, fast. Evidence over slideware — including evidence that something shouldn't be built.
Productionize
Permissions, integration, cost control, evaluation, monitoring. The unglamorous engineering that separates a demo from a product.
Enable
Your team runs it, understands it, and extends it. You own the code, the infrastructure, and the knowledge.
What does an efficiency gain actually look like?
Specific and countable. A quote that took two days goes out in two hours. Three hundred invoices a month stop being retyped. The month-end pack is ready on the first instead of the fifth. A backlog nobody has had time to touch finally gets touched. We agree on which numbers matter before we build, take a baseline, and report the same numbers afterwards — including when the movement is smaller than we hoped.
Where does AI actually help in a business like ours?
Almost always in the same places: information moving between systems, documents being read and re-keyed, questions whose answers already exist somewhere in your files, and reports assembled by hand from data you already hold. It helps far less with anything requiring negotiation, relationships, or judgment about exceptions — and we'll tell you which side of that line your problem falls on.
How is this different from hiring a development shop?
A development shop builds what you specify. We'll tell you when the specification is wrong — including when the honest answer is that a $50-a-month tool already does it and you should go buy that instead. We also don't consider a project finished at deployment. It's finished when your team can run it without us.
We're a small company. Are we too small for you?
Small and mid-sized companies are half of what we do, deliberately. The work is often better at that scale: fewer approval layers, a decision-maker in the room, and results visible in weeks rather than after a two-year program.
We don't have clean data or an IT team. Can we still do this?
Usually yes. Nobody's data is clean. The assessment tells you honestly whether the gap is a speed bump or the whole project, and we build for the team you have rather than the team a vendor wishes you had.
Who owns what you build?
You do — code, infrastructure, models, and documentation, in your accounts. We don't hold anything hostage, and the documentation is written for whoever inherits it.
If you'd rather talk than scroll, we'd rather talk too.
Tell us what you're trying to build, or which part of your week takes the longest. Any detail helps — we'd rather come to the call with something specific than a slide about ourselves.
vishwa@tapai.tech
Make it real. Make it faster. Make it yours.
We help at whichever stage you're at — an idea that needs building, an operation that needs sharpening, or a team that needs to run it all without us.
1/3
Build the thing you can't yet build
You have a product idea and a clear sense of who needs it. What you don't have is the engineering to make it real — or the certainty that what you'd build would survive its first hundred users. Come work with us. We'll pressure-test the idea, put a working version in front of real people in weeks, and build the version you can put your name on.
Who it's for
Founders with a validated problem and no engineering team
Domain experts who know exactly what should exist in their industry
Small teams whose prototype keeps breaking under real conditions
Operating companies launching a product line that has to stand on its own
What you get
A validated scope and an honest read on what shouldn't be built
A working product in front of real users in 8–12 weeks
Code, infrastructure, and documentation you own outright
A team that hands over cleanly, or stays as long as you need
2/3
Get more out of the week you already have
Quotes that take two days should take two hours. Invoices shouldn't be retyped. Monday's report shouldn't take until Tuesday. Come work with us. We'll find the two or three places AI genuinely moves the numbers, build them properly, and show you the before-and-after.
Who it's for
Companies of roughly 10–200 people where the constraint is hours and turnaround time
Owners who know exactly which process is slowest and have nobody to hand it to
What you get
A written map of where the time goes and what's worth automating
A baseline of the numbers before we start
Two or three working systems, not twelve pilots
Integration with the tools you already run
Fixed scope and fixed price, agreed before we start
3/3
Make the gain stick
An efficiency gain nobody trusts lasts about a month. So we build the capability alongside the software: your team learns what the tools do well, where they fail, and how to tell the difference. The result is a system people actually use — which is the only kind that saves anything. Come work with us; this is the part most firms skip.
Who it's for
Any team about to have AI in their daily work
Every client we finish a build for
What you get
Training grounded in your actual workflows, not generic prompt courses
One or two internal champions who can carry it forward
A short usage policy your team helped write
A handover your people can act on
AI enablement, in detail
Observe
1–2 weeks
We spend time with the work as it actually happens — which is almost never how the process document describes it. You get a written recommendation: what to build, what to skip, what it costs, what it's worth, and the baseline numbers we'll measure against. Plenty of clients read it and decide to build less than they planned. That's a good outcome.
Prototype
2–4 weeks
One narrow, working slice in real hands. Weekly demos, an environment you can log into from week two, no phase where you're waiting in silence. If it doesn't hold up, you've spent weeks finding out instead of quarters.
Productionize
6–12 weeks
Authentication and permissions. Integration with your existing systems. Cost ceilings so model spend can't quietly triple. Evaluation so you know when quality drifts. Monitoring so someone is alerted before a customer notices. Rollback, so a bad day is a bad hour.
Enable
Runs alongside, not after
Training built on your own workflows. Champions identified and coached. Documentation written for humans. A walkthrough with whoever runs it next — your first engineer, your ops lead, or us on a retainer you can end.
Tell us which stage you're at and we'll tell you what we'd do first.
BUILD IT
The distance between a demo and a product is where we work.
A prototype takes a weekend. Something that survives real users, real data, and real failure takes engineering discipline most early teams don't have in-house yet. We supply it — as a build partner, not a staffing invoice.
This is a fit if you're—
A founder with a validated problem and no technical team yet
A domain expert who knows what should exist in your industry, but not how to build it
A small team with a prototype that keeps breaking under real conditions
An operating company launching something that has to stand on its own
It's not a fit if you want a fixed spec built without pushback, a two-week build of a six-month product, or bodies on a contract. We'll say so early rather than take the money.
Idea → Validate → Build → Harden → Launch
Validate1–2 weeks · $12,000
We interrogate the idea before building it. Who has this problem badly enough to pay? What already exists? What's the smallest version that proves it? You leave with a scope, a build plan, and a number. Some clients read it and decide not to build. For $12,000, that's a bargain.
Build6–10 weeks
A real product in front of real users. Weekly demos, working software from week two.
Harden3–6 weeks
Production readiness in the literal sense — see the checklist below.
Launch and hand over
Your accounts, your repositories, your credentials, your documentation.
The list most prototypes fail
It keeps working when the model returns something wrong
Only the right people can see the right data
You know what it costs to run, and it can't quietly triple
Quality is measured, not vibe-checked
Someone is alerted before a customer notices
A new engineer can understand it from the documentation
It can be turned off, rolled back, and restored
Every product we ship clears this list. It's the whole difference between something that impresses in a demo and something you can put your name on.
Three ways to work together
Fee-for-build
Fixed scope, fixed price, you own everything. Cleanest when you're funded.
Hybrid
Reduced fee plus a minority stake. For founders with conviction and limited runway.
Fractional CTO
Technical leadership 4 days a month: architecture, hiring, vendor decisions, without the executive salary.
We'll tell you on the first call which one fits. We don't take equity in every project.
Send us the idea, however rough.
A paragraph is enough. If it's not something we should build, we'll tell you what should exist instead.
RUN IT BETTER
AI that fits a company your size.
No data science department. No eighteen-month transformation program. We find the two or three places AI measurably speeds up your business, build those properly, and make sure your team can run them.
We start with your week, not with the technology.
Every company has a handful of tasks that eat hours and produce nothing: retyping information between systems, hunting for a document someone filed somewhere, writing the same email for the ninetieth time, rebuilding Monday's report on Monday.
That's where the time is, and that's where AI pays for itself fastest. We find it by watching how the work actually happens — not by reading the process document.
What we build
Quotes and proposals
Drafted from your price list, past jobs, and the customer's request. Your team reviews and sends instead of starting from a blank page. Typical result: turnaround from days to hours.
Paperwork that reads itself
Invoices, purchase orders, contracts, intake forms. Pulled in, read, checked, pushed into your system — nobody retypes a line.
Answers from your own files
Ask a question in plain language, get an answer from your manuals, policies, past projects, and contracts — with a link to the source, and only from documents that person is allowed to open.
Inbox and request handling
Email, forms, and tickets sorted, summarized, drafted, and routed, with the urgent ones flagged before anyone opens the app.
Reporting that builds itself
The Monday report, the month-end pack, the customer summary — assembled from the systems you already use, ready before you get in.
Forecasting and scoring
Which customers are about to churn, which jobs will run over, what demand looks like next quarter. Built from your history, not an industry average.
We agree on the numbers before we build.
Efficiency claims are easy to make and rarely checked. So we start every engagement by writing down what we're trying to move and what it looks like today:
Turnaround — how long a quote, an order, or a case takes from arrival to done
Volume — how much the same team can process in a week
Touches — how many times a piece of information gets handled or re-entered
Accuracy and rework — how often something has to be corrected downstream
Time-to-answer — how long it takes someone to find what they need
We take a baseline, build, and report the same numbers afterwards — including when the movement is smaller than we hoped. Recent engagements have moved quote turnaround by 68%.
If a $40-a-month tool solves your problem, we'll name the tool and send you on your way. If your data isn't in a shape that supports what you want, we'll tell you what to fix first. If the honest return doesn't justify the build, we'll put that in writing. A short assessment that prevents a bad six-figure decision is worth more than a project we shouldn't have sold you.
Where your information goes
Your data stays in your accounts. We build inside your cloud environment, not on a platform you rent from us. Access follows the permissions you already have — if someone can't open a folder today, they can't get answers from it tomorrow. Before you sign anything, we document which models see what, where it's stored, and how long it's kept.
What it costs
Readiness assessment $12,000 · First production workflow $45,000–$90,000 · Team enablement $18,000 · Ongoing support from $4,500/month
Fixed scope, fixed price, agreed up front.
MAKE IT STICK
The software is the easy half.
Most AI projects don't fail technically. They fail because the people expected to use them never got shown how, didn't trust the output, and quietly went back to the spreadsheet. Then the efficiency gain you paid for shows up in a report and nowhere else. We treat that as an engineering problem too.
Training on your work, not generic prompt courses
Sessions built on your real workflows and your real documents. People learn on the thing they'll actually use, which is the difference between a system that gets adopted in a week and one that gets worked around.
Champions inside your team
We identify one or two people who take to it and coach them properly. When we leave, someone in the building knows how the system works and why.
Guardrails your team helped write
A short, readable policy: what these tools should be used for, what they shouldn't touch, what a human has to check, and who to ask. Written with your people, not handed down.
Judgment, not just usage
The most valuable thing we teach is where these systems fail — plausible-sounding wrong answers, stale sources, confident nonsense. A team that can spot a bad output is worth more than a team that can write a clever prompt.
Why it's a service and not a footnote
Adoption is what turns a working system into a measurable gain. Software nobody trusts becomes a maintenance cost the moment we leave; a team that understands the tools keeps finding new uses for them long after the project closes. That's why this is a track and not a line item — and why we'd rather you never need to call us again.
Tell us who's on the team and what their day looks like.
Selected work.
AI, machine learning, and data systems built across regulated industries, enterprise operations, and research.
Financial Services
Bank Risk Intelligence
AI pipelines extracting risk and exposure signals from banking documents, tables, and unstructured data.
Financial Report Generation
Generative AI workflows combining economic data and source documents to accelerate analyst reporting.
Systemic Banking Monitoring
Agentic data pipelines for banking information extraction and anomaly detection.
Healthcare & Regulatory
Import Risk Prediction
Production ML models supporting risk prediction and operational decisions for regulated imports.
Model Monitoring
Anomaly detection and concept-drift tracking for production machine-learning systems.
Medical Knowledge Search
NLP-powered search for pharmaceutical and healthcare knowledge repositories.
Leadership & Workforce
AI Leadership Assistant
A multi-channel virtual assistant spanning email, SMS, Slack, and Teams.
Leadership Intelligence
ML pipelines turning company and leadership data into measurable performance indicators.
Smart Calendar Intelligence
Models classifying meetings and suggesting future events aligned to priorities.
Commerce & Marketplaces
Product Classification
Computer vision and NLP for automated inventory classification and taxonomy design.
Recommendation Engine
Behavior-based recommendations matching marketplace users with relevant inventory.
What we've learned putting AI into real businesses — including the things that didn't work.
The honest AI readiness checklist for a 50-person company
A genuinely useful self-assessment.
How to measure whether an AI project actually saved you anything
Our measurement discipline, as an argument.
What "production-ready" means for an AI feature
The checklist from the Build It page, expanded, for technical readers.
Why TapAI exists.
Two kinds of good ideas stall for the same reason. A founder builds something clever that can't survive its first hundred users. A 60-person company knows exactly which process is slowest but has nobody to hand it to.
Both need the same thing: engineers who can take something from working-once to working-every-day. That's the entire company.
What we believe
Start with the work, not the technology
The useful automation is never where the process document says it is.
Measure it or don't claim it
Baseline before, measure after, report both.
Working software over documents
You'll see something running in week two of every engagement.
Build to hand over
If you still need us in a year, that should be your choice.
Say the unprofitable thing
Including "don't build this," "buy the cheap tool," and "your data isn't ready."
Say hello.
Tell us what you're trying to build, or which part of your week takes the longest. Include any detail or backstory that helps — it means we come to the call with something specific rather than a slide about ourselves.
Thanks — Ravi will reply from vishwa@tapai.tech
Reply to that email with anything you'd like us to look at before we talk.