[ Output you can ship without rewriting it ]
Systems that write, summarise, translate and draft at volume — trained on your tone, grounded in your facts, and reviewed before anything reaches a customer. The hard part is not generating text. It is generating text nobody has to fix.
Systems that write, summarise, translate and draft at volume — trained on your tone, grounded in your facts, and reviewed before anything reaches a customer. The hard part is not generating text. It is generating text nobody has to fix.
Show us twenty pieces you are happy withWe build a style guide the model actually follows — examples, banned phrases, sentence length — instead of asking it to "be professional" and hoping.
Prices, specifications and policies are retrieved, never invented. If the model does not have the fact, it says so rather than filling the gap.
Where the output feeds a system, it comes back as validated JSON with a schema. Parsing free text out of a paragraph is how these pipelines break at 3am.
Prompts are code: reviewed, versioned and tested against a scored set, so a tweak that helps one case cannot silently break nine others.
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.
Product descriptions, category copy, listings and localisations generated from your own catalogue data, in your tone, with a review queue in front of publication.
Long documents, call transcripts and ticket threads turned into the three things someone actually needs — with the source passage attached so it can be checked.
Reply drafts, proposal sections and email variants generated where the work happens — in the CRM or the admin panel — so nobody has to copy text between tabs.
Twenty pieces your team is happy with, and five they are not. That contrast teaches the system far more than a description of your brand ever will.
A scored set of real inputs with the output you would accept, written before any prompting. Without it there is no way to tell a change from an improvement.
Catalogue, pricing, policies and past work indexed so the model retrieves rather than recalls. This is what stops a confident wrong answer.
Schemas, length limits, required sections. A generator that can return anything will eventually return something that breaks whatever consumes it.
Draft, review, publish. The review step shrinks as the scores hold up, and it is what makes the first month survivable.
The eval set runs on a schedule. Model providers ship updates you did not ask for, and this is how you find out before your customers do.
Generative AI is easy to demo and hard to keep. The first output impresses everyone; the fiftieth is where you find out that the tone drifts, the prices are invented, and someone on the team has quietly gone back to writing it by hand. What separates a pilot from a system is the unglamorous half: examples, a scored test set, retrieval for anything factual, and a review step that shrinks as trust is earned.
We build the second kind. Techtaru has been shipping business software in Jaipur since 2012, and a generation pipeline is a pipeline first — queues, retries, validation, logs — with a model in the middle of it.
A model shown twenty pieces you approved will match your voice more closely than one told to write "in a friendly, professional tone".
Anything with a number or a promise in it is retrieved from your data and quoted. The model arranges facts; it does not supply them.
The draft queue is not a limitation to remove as soon as possible. It is where the feedback comes from that makes the next month better.
Models now return schema-valid JSON reliably, which means generation can feed a system directly instead of a human copying from a chat window.
You can paste a hundred pages into a prompt, but accuracy drops in the middle of it and you pay for every token every time. Retrieving the right three paragraphs is cheaper and better.
Classification, routing and extraction rarely need the frontier model. Using a small model for the eighty per cent and a large one for the rest is where the cost case is won.
Customers and marketplaces increasingly want to know what was machine-written. Building the provenance in now is cheaper than retrofitting it.
Google's position is about quality, not authorship — unhelpful content ranks badly whoever wrote it. What sinks sites is publishing hundreds of near-identical pages with nothing specific in them. We ground generation in your own catalogue and facts and keep a human review step, which is the difference between useful pages at volume and thin pages at volume.
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