Start from the work, not the model
We begin with the task your team repeats and how success is measured, then choose the simplest AI that does it. Sometimes that is a rule or a form, not AI.
How we build AI
How we build AI: a four-stage process with a test at every step
Every AI project we run goes through the same four stages: discovery, a prototype on your real examples, a pilot on a share of live work, and production with monitoring. Each stage ends with a go or no-go decision against targets we agree with you first, so you only pay for the next step when the last one has proved itself.
- Tested on your own data
- A person approves risky actions
- You can stop after any stage
Engineering team in Jaipur, India. Building software since 2019, 500+ projects delivered. Working with businesses in the US, UK, Europe, UAE, Australia and India.
Four rules every Prilient AI project follows
Prove it on your data
Every decision is tested on your own calls, chats, documents or records, not on a demo dataset.
A person stays in control
The AI knows its limits, hands over when it is unsure and waits for approval before risky actions.
Measure it after launch
Quality, speed and cost are tracked every month, because data, customers and models change.
Our AI development process, stage by stage
The stages are the same whether we build an AI agent, a voice or WhatsApp assistant, document AI, a generative AI app or a forecasting model. What changes is what we measure.
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Discovery
We map the workflow with the people who do it today, collect sample conversations, documents or data, and list the systems involved. Together we agree what the AI should do, what it must never do, and the numbers that will decide whether it is working.
You get: a one-page brief with the task, the limits, the data needed and the success targets, plus a test set of real cases.
Gate: is there enough good data, and is the value worth the cost? If not, we say so and stop here.
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Prototype
We build a working version on your real examples, connected to test copies of your tools, and score it against the test set. A prototype typically takes 2–4 weeks.
You get: a working prototype you can try, and a scorecard against the targets.
Gate: does it reach the agreed pass mark on the test set?
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Pilot
The AI first runs in shadow mode next to your team without acting, then handles a small share of live work while we monitor and correct it. Every failure goes back into the test set.
You get: a pilot report with accuracy, handover rate, speed and cost on real work.
Gate: does it hold up on live work, and does your team trust it?
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Production and monitoring
Full launch with dashboards, alerts and human handoff. We review quality every month, tune prompts and models, and retrain machine learning models when the data drifts.
You get: monthly reports and support.
Gate: every change is re-tested against the test set before it is released.
Not sure which AI project to start with? Our AI consulting team ranks your use cases first.
How we test AI before it reaches your customers
Accuracy alone is not enough. We agree a short scorecard for each project, build a test set from your real cases, and score every version of the prompts, model or pipeline against it. We also try to break it on purpose, with off-topic questions, misleading inputs and prompt-injection attempts.
| What we build | What we measure | How we try to break it |
|---|---|---|
| AI agents and chatbots (voice, WhatsApp, web) | Correct answers, task completed, handover when it should, tone, response time, cost per conversation | Off-topic and trick questions, angry customers, attempts to make it ignore its instructions |
| Document AI | Field-level accuracy, documents needing human review, processing time | Poor scans, handwriting, new layouts, missing pages |
| Generative AI and search on your documents | Answers backed by a cited source, "I don't know" when the answer is not in the documents | Questions outside the documents, conflicting sources |
| Forecasts and machine learning models | Error against past periods held back from training (backtesting), drift in incoming data | Unusual months, missing data, sudden changes |
Every mistake found in review becomes a new test case, so the same error is caught before the next release.
Responsible AI, built into the process rather than added at the end
Responsible AI is a set of design decisions we make in discovery and check at every gate.
- Clear limits. Each AI has a written list of what it may do and what it must hand over.
- Human approval for risky actions. Refunds, payments, record changes and anything hard to undo can require a person's approval.
- Checks for unfair outcomes. In hiring, lending or collections, we check results across groups before launch and keep a person in every final decision.
- A record of every decision. Conversations, tool calls, handovers and approvals are logged, so you can see why the AI did what it did.
How we handle your data
- We agree in writing which data the AI may use, and keep it to what the task needs
- Personal data is masked in logs where possible
- Each AI connection has its own account with the least access it needs
- Keys and passwords live in a secrets manager, never in prompts or code
- Project data is deleted on request when the work ends
- We sign an NDA before we see your data
For how this website handles personal data, see our privacy policy.
Built for the AI and privacy rules your customers live under
We flag in discovery which rules apply to your use case and design for them from the first prototype.
| Rule | What it means for your AI project |
|---|---|
| EU AI Act | Since 2 August 2026, people in the EU must be told they are interacting with an AI system unless it is obvious. Uses the Act treats as high-risk, such as hiring and credit decisions, carry extra duties from 2 December 2027. |
| EU GDPR and UK GDPR | A lawful basis for using personal data, only the data the task needs, and people's rights to access and deletion. |
| India's DPDP Act | The DPDP Rules were notified on 13 November 2025; the main duties on notice, consent, security and breach reporting apply from 13 May 2027. |
| WhatsApp and telecom rules | Opt-in before the first message, approved templates outside the 24-hour window, and India's DLT and TRAI rules for outbound calls and SMS. See WhatsApp AI chatbot development and AI voice agents. |
This is general information, not legal advice.
A small senior team that stays with your project
Project lead
Your single point of contact: runs the weekly demo, tracks the targets and raises risks early.
AI engineer
Designs the prompts, models, retrieval and test sets, and owns the scorecard.
Software engineer
Builds the connections to your systems, the dashboard and the approval steps.
QA and review
Runs the test set, tries to break the AI before your customers do, and reviews live conversations during the pilot.
You see progress in a weekly demo, not a status report. We work from Jaipur, India, with daily overlap with UK, Europe and UAE working hours.
We use the same process on the AI we run ourselves
Own product · AI agents
Prilient AI Agents
Six AI agents (Receptionist, Customer Support, WhatsApp Lead Generation, Appointment Setter, Debt Collection and HR Interview Screening) that hand work to each other and to people, with every conversation, handover and tool call logged.
See Prilient AI Agents
Own product · AI business software
VyapaarSense
Our AI business management software for small and mid-sized businesses, with document AI, bank reconciliation and cash-flow forecasts inside everyday records.
See VyapaarSense
Own product · Workforce software
Tainaat WorkforceOS
Our compliance-first workforce software for security, facility-management and logistics companies in India, with Aadhaar e-KYC, GPS and biometric attendance and statutory payroll.
See TainaatWe also built DataSwitch for a client: an AI document extraction platform that turns uploaded PDFs and images into structured, checkable data.
Questions about how we build AI
What is your AI development process?
We build AI in four stages: discovery, a prototype on your real examples, a pilot on a share of live work, and production with monitoring. Each stage ends with a go or no-go decision against targets agreed at the start, so you only fund the next stage when the last one has proved itself. The same process applies to AI agents, voice and WhatsApp AI, document AI, generative AI apps and forecasting models.
How long does it take to build an AI solution?
A working prototype on your own examples typically takes 2 to 4 weeks after discovery. The pilot and production stages depend on how many systems the AI connects to and how much live traffic it needs to prove itself on. We give you a time and a fixed price for each stage before it starts.
How do you test AI before it goes live?
We build a test set from your real cases, such as past calls, chats, documents or sales history, and agree the pass mark with you before we build. Every version of the prompts, model or pipeline is scored against that set, and we also try to break it with off-topic, misleading and prompt-injection inputs. Then the AI runs in shadow mode next to your team before it handles any live work on its own.
What happens when the AI gets something wrong or is not sure?
It is designed to hand over rather than guess. Each AI has clear limits: when a request is outside them, when its confidence is low or when the customer asks for a person, it passes the case to your team with the context. Risky actions such as refunds, payments or record changes can require a person's approval. Every mistake found in review goes into the test set, so the same error is caught before the next release.
How do you handle our data and keep it private?
We agree in writing which data the AI may use, and we keep it to what the task needs. Personal data is masked in logs where possible, access is limited to the people and services that need it, and project data is deleted on request when the work ends. We design each solution to help you meet the privacy law that applies to you, such as the EU GDPR, UK GDPR or India's DPDP Act.
Does the EU AI Act apply to AI you build for us?
If your AI serves people in the EU, it can. Since 2 August 2026, Article 50 requires that people are told they are interacting with an AI system unless that is obvious. Uses the Act treats as high-risk, such as hiring and credit decisions, carry extra duties from 2 December 2027. We flag which category your use case falls into during discovery. This is general information, not legal advice.
Can we stop after the prototype?
Yes. Every stage ends with a go or no-go decision. If the prototype does not reach the targets we agreed, we tell you plainly, and you can stop, change the scope or try a different approach. You keep the findings, the test set and the code from the stages you paid for.
Start with a brief, not a big commitment
Tell us the task you want AI to take on. We will tell you whether AI is the right tool, what we would test it on, and what the first gate would look like.