Roadmap
What is live, what we are building, and what comes later.
AI now answers your customers and does parts of your work, but sometimes it is wrong and nobody notices.
We check your AI, fix what hurts, and keep it healthy, and a named person signs every finding.
We also build the business software you run on, where AI only drafts and you approve.
Where everything stands
Here is what you can use today, what we are making now, and what waits until it is paid for.
Open for bookings The Clinic: AI Checkup, Treatments (the fix) and the Care Plan
- Live
- AI Checkup · INDRA · The Lab · Vritanta AI · TaraLand · Homestay hub · Agent testing (Shiva): Live, on its own site (opens another site)
- Building
- Conversation reading, inside the Checkup · INDRA for gyms
- Designed
- Automatic weekly re-tests, for Care Plan clients · Your own software, built on INDRA · Router · Helper network
- Later
- Arena
Live means you can use it or buy it today, because it is already built and open. Building means we have approved it and are making it now, so you cannot use it yet.
Why we are building this
Businesses are putting AI to work faster than anyone can check it, and the mistakes are quiet ones.
The words we use
- Agent
- An agent is an AI program that carries out tasks for you, such as answering customers or sending invoices.
- Signed record
- A signed record is a note of what happened, sealed with a digital signature so any later change shows.
- INDRA
- INDRA is our own business software, and its live demo uses example businesses with made-up data.
- Shiva
- Shiva tests AI agents before they go live, and it now has its own website, linked here.
What goes wrong
- A chatbot quotes a wrong price or invents a policy, and a customer holds you to it.
- An automation stops working on a Tuesday, and nobody finds out until the month-end numbers look strange.
- An agent reports that a job is done when nothing happened, and we have seen that in our own company.
So we are building a way to test AI before it goes live, watch it, and prove what we found.
- What we do
- We check the AI that talks to your customers, fix what is broken, and keep watching it afterwards.
- What it means for you
- You find wrong prices, false promises and silent failures before your customers do, instead of after they complain.
- Next step and cost
- Request a call and we start; the Checkup costs ₹9,999 (draft price), credited in full if you book a treatment.
- How you can check us
- Open the free INDRA demo and the example report on the Clinic page, then compare them with what we say.
What we do for you
Here is everything we offer or are making, with its true state, so you can see what is ready today.
- Live
Live means you can use it or buy it today, because it is already built and open.
- Building
Building means we have approved it and are making it now, so you cannot use it yet.
- Designed
Designed means we have written the plan down, but we have not started to build it yet.
- Later
Later means we will not start it until the money from our other work pays for it.
AI Checkup
LiveWe study how your business and your AI behave, then write a short report on what to fix first.
Price: ₹9,999 (draft price)
INDRA business software
LiveINDRA is our business software for bills, books, customers, staff and your website, where AI drafts and you approve.
Price: The demo is free to open
The Lab, free tools
LiveThe Lab has free tools for PDFs, images, video and text that run inside your browser, so nothing is uploaded.
Price: Free
TaraLand
LiveTaraLand is a calm play-and-learn world for children, with no ads, no accounts and no tracking of any kind.
Price: Free
Vritanta AI
LiveVritanta AI answers questions by voice or text, using only the words on our own published pages.
Price: Free
Conversation reading, inside the Checkup
BuildingSoftware reads real conversations with your chatbot and flags where it loops, gives wrong prices, or claims unfinished work.
Price: Price when it opens
Agent testing (Shiva)
LiveAgent testing now lives on Shiva's own website; open it there to read what it does today.
Price: Price on request — ask us
INDRA for gyms
BuildingA version of INDRA for gyms, handling members and batches, where AI drafts reminders and you approve them.
Price: Price when it opens
Automatic weekly re-tests, for Care Plan clients
DesignedEvery problem we find becomes a test, re-run every week and again whenever your AI provider changes its model.
Price: Price when it opens
Your own software, built on INDRA
DesignedWe plan to build software shaped to your business on top of INDRA, aiming for weeks rather than months.
Price: Price when it opens
Try the INDRA live demo to see the Live software with example businesses and made-up data.
Draft price These prices are drafts, not final. Nothing is charged without a written quote that you accept first. Prices in dollars are for clients outside India.
- What we do
- We offer checks and care for your AI, and we make business software where AI only drafts.
- What it means for you
- You can see at a glance what you can use today and what is still being made.
- Next step and cost
- Request a call about a Live item; the Checkup shows ₹9,999 and the Care Plan says ask us.
- How you can check us
- Each card shows its status and its price, and the Live ones link to something you can open right now.
How a Checkup works, step by step
A Checkup is a short, fixed job, and this is each step of it, with who does what.
The Checkup has seven steps, from the first call to your decision, and each step names who does it. The part where software reads your chatbot's conversations is still being built, and data terms are not written yet.
- A personLiveThe callYou tell us what your AI does and what worries you, and we ask how your work really runs.
- A personBeing writtenYou say what we may seeWe explain what we take and why, and you agree in writing before we look at anything.
- SystemBuildingPrivate details come outNames, phone numbers and health details are removed before anything is stored, so our notes never hold them.
- Agent draftBuildingSoftware suggests problemsOur software suggests possible problems, such as loops, wrong prices and false claims of finished work.
- A personLiveA named person checks each oneA named person checks every suggestion by hand, and nothing reaches you until that person has signed it.
- A personLiveYour reportYour report says what we found, what it means for you, what to do next, and how to check us.
- A personLiveYou decideNothing changes in your systems without your yes, and you can undo any change we make with you.
- What we do
- We run a Checkup in seven steps, and a named person answers for what the report says.
- What it means for you
- You get a short, plain report you can act on, and your systems stay untouched until you agree.
- Next step and cost
- Request a call and we start; the Checkup costs ₹9,999 (draft price), credited in full if you book a treatment.
- How you can check us
- Compare the example report on the Clinic page with the steps here, and ask us anything on the call.
Our promises, and what we never do
These are the promises we make to every client, and each one is something you can hold us to.
- You see the full price in rupees before you book, and no fees are added later.
- A named person signs every finding, because AI helps us look but a person answers for the result.
- We tell you what we could not check and what we could not fix, so you never guess.
- Every report carries proof you can check yourself, which we explain in the section on how you can check us.
- Your data stays yours: we collect only what the work needs, and we never sell or share it.
- If a problem we find could protect other businesses, we share only a made-up copy, and only with your OK.
- AI only drafts, people approve, and you can undo any change that we make together with you.
- We mark every example as Example, and we show no fake reviews, no invented clients and no made-up numbers.
- If you do not understand something we wrote, that is our mistake, and we will rewrite it.
We have not written our file-deletion terms yet, so ask on the call and we will write them down.
What we will never do
There are also four things we will never do, and no reason or request will ever change that.
- We never send a message, publish anything, make a payment, or change your systems without a person saying yes.
- We never test a real system unless you have given us written permission to test that system.
- We never sell or share your data, and we never show a price that grows at checkout.
- We never put ads, tracking or accounts into TaraLand, which is our calm play-and-learn world for children.
- What we do
- We keep a short list of promises, and we write them down so that you can hold us to them.
- What it means for you
- You know the price, who is responsible, and what we will refuse to do before you hand us anything.
- Next step and cost
- Read the care page for what is confirmed today; every price stays a draft until we confirm it.
- How you can check us
- Read them against our care page, which shows only the promises we have already written and confirmed.
How you can check us
We do not ask you to trust us, so each report is built to let you check our work yourself.
A signed record
A signed record lists what we looked at and what we found, and you keep its final signature. After you hold that signature, even we cannot quietly change the record without the change showing.
A before-and-after
Each fix comes with a before-and-after, so you can see exactly what changed in your own systems.
A test you can run again
Each problem we find becomes a test, and you can run it again whenever you want to check.
A named person who signs
A named person signs every finding, so a human being answers for it and not only a program.
The honest limits
This is the limit of what our records can prove, and we state it plainly.
“anything you already hold, or older than the latest third-party timestamp, cannot be changed without detection.”
- A third-party timestamp is a dated stamp from an independent service that shows when a record already existed.
- A newer record, or one you have not yet received, could still be changed by us before it is stamped.
- The tools that make all this strongest belong to our agent testing work, which is still being built.
- What we do
- We build each report so that a stranger could check it, not only the person who wrote it.
- What it means for you
- You can verify our work without our help, and that means you never have to rely on our word.
- Next step and cost
- Ask for the signed record, the before-and-after and the test; they come with the report at no extra charge.
- How you can check us
- Check this very page: every number has a source at the bottom, and every status says what is built.
How the pieces fit together
Here is the whole platform on one page, from your business at the top to the fixes at the bottom.
Six stages run from your business, through agent testing and one signed record, to a person and the fix. Only your business at work is live today; the middle stages are being built, and the helpers are designed.
- SystemLiveYour business at workYour business runs on INDRA or on a bot of its own, and that is where the work starts.
- SystemBuildingAgent testingBefore an agent goes live, we test it safely with fake companies and fake keys, never real systems.
- Signed receiptBuildingOne signed recordEverything the testing does goes into one signed, linked record that nobody can quietly edit.
- Agent draftBuildingSoftware reads and draftsOur software reads real conversations and the record, then drafts a finding that a person must confirm.
- A personBuildingA person confirms, then we treatA named person confirms each finding, and only then do we fix the problem with your approval.
- A personDesignedHelpersInvited helpers test and fix problems for pay, working only on made-up examples, never on your data.
Source: our platform plan, version 1.1 of 27 October 2026, with every status as it stands today
The parts and where each stands
| Part | What it does | Status | Price |
|---|---|---|---|
| Router | It decides which AI model answers each request, using your written rules first and a fallback model if one fails. | Designed | Price when it opens |
| Helper network | It is an invite-only group of testers and fixers, working on made-up scenarios only and paid for confirmed work. | Designed | Price when it opens |
| Arena | It would publish signed rankings of agents on Indian business tasks, with error bars so that nobody is misled. | Later | Price when it opens |
Anything here that is not live says Price when it opens, because we will not guess prices for unbuilt work.
- What we do
- We build in a set order, starting with the Clinic, then agent testing, and then the rest.
- What it means for you
- Each piece you can use today is marked Live, and the rest show honestly how far along they are.
- Next step and cost
- Apart from the Care Plan, everything not yet live says Price when it opens, so you can plan without surprises.
- How you can check us
- You can check Live items yourself: open the INDRA demo, the Lab, TaraLand and Vritanta AI right now.
Why we can build this
We think four things set us apart, and each is something you can look up, not take on trust.
We already own the hard parts
Our signing code already seals each log entry with a digital signature and links it to the one before it. So far only our own test agents use it, and no customer runs on it yet.
We are our own first patient
We ran our own company on AI agents first, and our About page tells what went wrong.
A person signs, and we speak to owners
A named person signs our work, and we speak to business owners in plain words, not only to engineers.
Defences for behaviour, not only attacks
Shared defences exist for attacks, but we found none for loops, false done claims, wrong prices or broken hand-offs. Immunity and Agent Threat Rules are two public projects that share defences against attacks; we link both in the sources.
Sources: Immunity protocol; Agent Threat Rules (Help Net Security, 3 June 2026).
- What we do
- We learned by running our own company on AI agents, and we show our failures before asking for trust.
- What it means for you
- You work with people who have already made these mistakes themselves and who know what to look for.
- Next step and cost
- Request a call and ask about our failures; if it fits, the Checkup is ₹9,999 (draft price).
- How you can check us
- Our own story, with its numbers and their dates, is on the About page for you to read.
The science we stand on
We build on published research, and we name the study so that you can read it and judge for yourself.
One study found GPT-4 got a prime-number question right 84% of the time in March 2023, but 51% in June.
Source: How is ChatGPT's behavior changing over time? arXiv 2307.09009
Why agents fail
A study of 1,642 recorded agent runs found 14 different ways that agents fail, grouped into three kinds.
An AI judge agreed with people at 0.77 to 0.79, where 1.0 is perfect; two people agreed at 0.88.
Our list of failure types starts from this study, and a person checks every label before you see it.
Source: Why Do Multi-Agent LLM Systems Fail? (MAST), arXiv 2503.13657
Finding the failing step
In three studies, the best automatic methods found the exact failing step only about 5% to 45% of the time.
So a person confirms every finding, and we will never sell you a fully automatic diagnosis of your AI.
Source: Which Agent Causes Task Failures and When? (Who&When), arXiv 2505.00212; TRAIL: tracing agent errors, arXiv 2505.08638; Where LLM Agents Fail and How They Can Learn From Failures (AgentDebug), arXiv 2509.25370
Reliability
In the tau-bench test, GPT-4o passed all eight repeats of the same task on fewer than 25% of retail tasks.
So every test we run is repeated four times, to check that a result is not luck.
A problem counts as real when it appears in two of the four runs, or in one run if critical.
Source: tau-bench, arXiv 2406.12045
Drift
GPT-4's accuracy on one task fell from 84% to 51% in three months, as the chart above shows.
Anthropic's September 2025 report described three bugs that degraded Claude's answers for weeks before its own checks noticed.
After every change of AI model, the Care Plan will run a small set of tests to see what moved.
Source: How is ChatGPT's behavior changing over time? arXiv 2307.09009; Anthropic, a postmortem of three recent issues (September 2025)
Routing
Early papers claimed big savings from routing, but 2026 studies found commercial routers do not reliably beat a simple baseline.
A router is a program that picks which AI model answers each request, to save money or raise quality.
So our router follows your written rules first, and it will not learn until a test shows that helps.
Source: FrugalGPT, arXiv 2305.05176; RouteLLM, LMSYS blog (1 July 2024); LLMRouterBench, arXiv 2601.07206; Dynamic LLM Routers are Often Misguided, arXiv 2610.02762 (preprint)
Rankings
The Leaderboard Illusion paper shows how public AI rankings can mislead when some entrants get extra private tries.
We would rank with a standard method called Bradley-Terry, and we would show error bars on every rank.
We also keep the rankings apart from our paid fix work, so that money cannot buy a better place.
Attacks
OWASP and MITRE publish public lists of attacks on AI agents, and AgentDojo and InjecAgent are public test sets.
These lists feed the attacks our agent testing will run against your agent, and we will rotate them often.
Source: OWASP Top 10 for Agentic Applications for 2026; MITRE ATLAS explained (Speakeasy); AgentDojo, arXiv 2406.13352; InjecAgent, arXiv 2403.02691
Proof
Signed records use ed25519, a standard digital signature, plus chained entries and independent timestamps in a public format.
You will hold a copy of each checkpoint, and an independent service will timestamp a daily one.
Slow requests
Sending a slow request twice can cut waiting time, but only when repeating it is safe.
We repeat a request only when that is safe, and never when it triggers an action such as a payment.
Source: The Tail at Scale, a secondary write-up of the paper by Dean and Barroso
- What we do
- We read the research on how AI fails, and we turn each finding into a rule we can test.
- What it means for you
- You get methods that were chosen because evidence supports them, not because they sound clever or modern.
- Next step and cost
- Reading is free, so follow any link that interests you and bring your questions to the call.
- How you can check us
- Each row names its study and links to it, and the full list is under Sources at the end.
How we compete with big companies, honestly
Big companies have more money and data than we do, so we say plainly where we win and lose.
Where we win
- We build for the owner of a small business, using words that owner can read without an engineer.
- Our work is governed, signed and made for India first, with a named person signing each result.
- We build from real failures, starting with our own, instead of from a list of features.
Where we lose
- Giants offer far more AI models than we do, and they get new models on the first day.
- They see far more traffic, so they learn more, and they give you one bill and staff around the clock.
- If you only need your data kept inside India, the large cloud companies AWS and Azure beat us.
What we will never do
- We will never fight on price, and we will never give away AI usage to win a customer.
- We will never build a catalogue of five hundred models, and we will never claim fully automatic results.
The tools that watch AI were bought
Tools that watch how AI programs behave have been bought up: Cisco bought Galileo, and ClickHouse bought Langfuse.
Dynatrace agreed in August 2026 to buy Arize, and Palo Alto Networks announced it would buy Portkey.
As far as we found, they serve engineers, and none serves the owner of a WhatsApp bot.
Sources: Cisco announces the intent to acquire Galileo; Two acquisitions deep: what happens to open-source LLM observability (Hackernoon, a secondary report on ClickHouse and Langfuse); Dynatrace to acquire Arize (press release); Palo Alto Networks to acquire Portkey (India Entrepreneur).
- What we do
- We stay small and narrow on purpose, and we aim at one job the giants have left alone.
- What it means for you
- You get a supplier that tells you straight where a bigger company would serve you better.
- Next step and cost
- Sending the request form costs nothing, and if a bigger company fits you better, we will say so.
- How you can check us
- Check any claim we make against the vendors' own pages, which we list under Sources.
How our own test platform works (technical)
Under our products is one rule: the software drafts, a person approves, and there is a record. We built a research platform to test that rule, and we keep it internal.
- Agent mesh (Elixir/OTP, a way of running many small programs that restart themselves when one crashes).
- Each agent runs as a supervised process: if one crashes, its supervisor restarts it and the others carry on. It runs on our own server, for our own test agents. No customer uses it.
- Signing kernel (Rust).
- It signs each agent action with an ed25519 key, a standard digital signature, and links it to the one before, so a changed or missing record shows. So far it has signed only synthetic test agents' actions.
- In our products.
- INDRA carries the same idea in its own code: every approved action leaves a signed, linked record you can check in its demo.
- SystemAn agent drafts the action
- A personA person approves it
- SystemThe kernel signs it with ed25519
- Signed receiptAnyone with the public key can check it
Internal research platform; no customer runs on it.
Sources
Every figure here comes from a source below, and our researchers opened and read each outside source first.
- Cisco announces the intent to acquire Galileo
- Two acquisitions deep: what happens to open-source LLM observability (Hackernoon, a secondary report on ClickHouse and Langfuse)
- Dynatrace to acquire Arize (press release)
- Palo Alto Networks to acquire Portkey (India Entrepreneur)
- our own agent journal, as of 26 July 2026
- Immunity protocol
- Agent Threat Rules (Help Net Security, 3 June 2026)
- Why Do Multi-Agent LLM Systems Fail? (MAST), arXiv 2503.13657
- Which Agent Causes Task Failures and When? (Who&When), arXiv 2505.00212
- TRAIL: tracing agent errors, arXiv 2505.08638
- Where LLM Agents Fail and How They Can Learn From Failures (AgentDebug), arXiv 2509.25370
- tau-bench, arXiv 2406.12045
- How is ChatGPT's behavior changing over time? arXiv 2307.09009
- Anthropic, a postmortem of three recent issues (September 2025)
- FrugalGPT, arXiv 2305.05176
- RouteLLM, LMSYS blog (1 July 2024)
- LLMRouterBench, arXiv 2601.07206
- Dynamic LLM Routers are Often Misguided, arXiv 2610.02762 (preprint)
- The Leaderboard Illusion, arXiv 2504.20879
- OWASP Top 10 for Agentic Applications for 2026
- MITRE ATLAS explained (Speakeasy)
- AgentDojo, arXiv 2406.13352
- InjecAgent, arXiv 2403.02691
- The Tail at Scale, a secondary write-up of the paper by Dean and Barroso
- our offer list and draft prices on the Clinic page