[ Models in production, not in a slide deck ]
We build AI into systems people already use — the enquiry inbox, the ops dashboard, the support queue — so it saves hours rather than impressing a boardroom. Fourteen years of building the software underneath is what makes the difference between a model that answers and a feature that ships.
We build AI into systems people already use — the enquiry inbox, the ops dashboard, the support queue — so it saves hours rather than impressing a boardroom. Fourteen years of building the software underneath is what makes the difference between a model that answers and a feature that ships.
Bring us the task your team repeats mostAnswers come from your documents and records, with the source attached, so a wrong answer can be traced instead of argued about.
Token cost per request modelled at the design stage. An assistant that costs more per query than the staff member it replaces is not a saving.
Anything that spends money, sends a contract or touches a patient record stops for approval. The model drafts; a person signs.
We record how long the task took before. If the AI version is not faster or more accurate, that is a finding, not something to hide.
Asked
What is the refund window on a bulk order?
Retrieved from your documents
Answered
Bulk orders over ₹50,000 can be returned
within 21 days of delivery, against the
standard 7 days. The goods must be unopened
and in original packaging.
The model arranged the sentence. Every fact in it — the amount, the window, the condition — came from a document you own, and the source is attached so a wrong answer can be traced rather than argued about.
Support and sales assistants that answer from your own documents, hand over to a person when they should, and never invent a price. Deployed on the site, on WhatsApp, or inside your admin panel.
The reading-and-retyping jobs: invoices into the ledger, CVs into the ATS, lab reports into the record, enquiries sorted and routed. Structured output, validated before it is written anywhere.
Features for the software you sell: search that understands a question, summaries, drafting, classification, recommendations. Built to your latency and cost budget, not to a demo.
A short, fixed-scope build to find out whether AI actually solves your problem, scored on your data against how your team does it today — before a budget is committed.
We look for the task your team repeats most and hates most — usually reading, sorting or re-typing something. That is where AI pays for itself first. Not every problem needs a model, and we will say so.
A narrow proof of concept on a real sample, scored against what your team currently produces. Two to three weeks, fixed scope, and a clear answer either way before anyone commits to a build.
Your documents, records and rules become the source the model answers from — indexed, chunked and cited. This is the step that turns a general model into one that knows your business.
The AI goes where the work already happens: inside the CRM, the admin panel, the WhatsApp thread. A separate tool people have to remember to open is a tool they stop opening.
Rate limits, prompt-injection defences, refusal handling, and an approval step on anything consequential. Every request and response logged, so a bad answer can be found and fixed.
Accuracy, cost and hours saved tracked after launch, and the prompts and retrieval tuned against what really gets asked. Models change under you; the monitoring is what tells you when.
Most AI projects fail in the same place. The demo works, everyone is impressed, and then it meets the messy data, the edge cases and the cost per request — and quietly stops being used. We start from the other end: what does this task cost you today, in hours, and what would have to be true for a model to beat that.
Techtaru has been building business software in Jaipur since 2012 — the CRMs, the portals, the booking systems. AI is a new capability inside that same craft, not a separate business. It matters more than it sounds: the hard part of an AI feature is rarely the model. It is the permissions, the data pipeline, the fallback when the model is down, and the audit trail someone will ask for six months later.
Answers come from your documents with the source attached. A general model that sounds confident about your refund policy is a liability; one that quotes the clause is useful.
The model drafts the reply, the quote, the diagnosis note. A human approves it. Automation earns its way past that gate over time, with evidence.
We model the token cost before we build. Knowing that a query costs ₹0.40 and replaces four minutes of someone's time is what makes the case, or kills it.
Capable models are an API call away and getting cheaper every quarter. The work — and the cost — has moved to the data pipeline, the evaluation and the integration around it.
For answering questions about your own business, indexing your documents and retrieving the right passage outperforms training a custom model, at a fraction of the cost and with citations you can check.
A system that can act on your behalf needs limits, approvals and a log before it needs more capability. We build the brakes in the first sprint, not after the first incident.
Without a scored test set you cannot tell an improvement from a regression, and a model update you did not choose can quietly break a feature overnight. The eval suite is deliverable, not overhead.
A proof of concept typically runs ₹1.5–4 lakh over two to three weeks and tells you whether the idea works before you spend more. A production assistant or automation usually lands between ₹4–15 lakh depending on how many systems it has to talk to, plus running costs for the model itself, which we model up front. Techtaru Digital quotes the build and the monthly running cost separately, so you can see both.
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