Generative AI Development for content, summaries and document work
Practical generative AI features inside your product or back office: drafting, summarising and extracting data from documents, with quality you can measure.
Generative AI is good at turning messy text into something useful: a summary of a long call, a draft reply, a table from a scanned form, a product description in three languages. It is poor at being trusted blindly. The work in a good project is mostly about checking and constraining the model.
We add generative features to existing products and internal tools, or build small standalone ones. Each project starts with a measurable question, such as how often the extracted invoice total is correct, and we keep testing against it after launch. We would rather ship a small feature that is right ninety times in a hundred and flags the rest than a clever one nobody trusts.
Where generative AI projects go wrong
A demo that does not survive real data
A prompt that works on three clean samples fails on scanned, skewed or handwritten documents. Real inputs from Indian businesses are rarely clean.
No definition of 'good enough'
Without a test set and a pass mark, teams argue about whether output feels right. Quality drifts and nobody notices until a customer complains.
Sensitive data sent carelessly
Pasting customer records into a public tool is a privacy risk. You need to know what leaves your systems, to whom, and under what terms.
Output used as final text
Generated copy goes live with invented facts, wrong claims or an unnatural tone. Content needs a review step and rules about what the model must not say.
Generative AI development we take on
Content drafting inside your tools
Product descriptions, email drafts, report sections or listing text, generated from your structured data and tone guide. Staff edit and approve, and the system learns what they change.
Summarisation
Call recordings, meeting notes, long threads and reports reduced to actions, decisions and open points. Hindi and Marathi inputs can be summarised into English or kept in the original.
Document data extraction
Invoices, KYC forms, purchase orders, mark sheets and lab reports turned into fields in your database, with confidence scores. Low-confidence fields are queued for a person.
Search and Q&A over your content
Staff ask questions about manuals, contracts or past tickets and get answers with citations. Access rules ensure people only see documents they are allowed to read.
Translation and localisation
English, Hindi and Marathi versions of catalogues and notices, with a glossary for your terms. Machine output is flagged for review where meaning matters, such as legal text.
Evaluation and monitoring
A test set built from your real documents, automatic scoring after every change, and a dashboard of accuracy and cost. This is what lets you improve with evidence.
How we build generative AI features
Discover
We gather real inputs, including the worst ones, and see how staff handle them today. We also check what data may be sent to an external model.
Define
We set the task, the fields or format required, and a pass mark. A hundred real examples with correct answers become the test set.
Design
We design where a person reviews, corrects and approves, and how corrections are stored so they can improve the next version.
Build
We try two or three models and prompt designs against the test set and choose by accuracy, speed and cost, not by reputation.
Ship & extend
We release to a small group, track accuracy and cost on live data, and adjust. Support covers one month, and new document types can be added later.
What it costs
A single generative feature is often close to the ₹1,00,000 AI chatbot and automation anchor, while larger document pipelines cost more. You receive a fixed written quote after discovery.
- Types and volume of documents
- Accuracy target and review screens
- Languages involved
- Integration with your product or ERP
- Monthly model usage, billed at actual cost
All prices exclude 18% GST. See also website costs and app costs.
Questions we get asked
Can generative AI read scanned and handwritten documents?
Often, yes, but accuracy varies with scan quality and handwriting. We test on your actual samples and report accuracy per field. Where it falls short, those fields are routed to a person for checking. Photos taken in poor light or at an angle lower accuracy, so a clearer capture screen helps.
How do you measure accuracy?
We compare model output to correct answers on a test set drawn from your real documents, and score each field. We repeat after every change, so you see accuracy numbers instead of impressions. You can see these numbers in a simple report after each test run.
Is our data used to train the model?
Major providers offer business settings under which API data is not used for training. We configure these, document the data flow, and can use a self-hosted model for highly sensitive material if the budget allows.
Will it write in our brand voice?
We give the model a style guide and examples from your own writing, and staff review before publishing. It gets close with good examples, but it still needs an editor for tone and factual checks.
How much does the model usage cost?
It depends on how many documents or requests you process and which model you choose. We estimate monthly usage from your volumes during quoting, and add caching and limits so the figure stays predictable. Caching repeated requests and using smaller models for simple tasks lowers the bill further.
Can you add this to our existing app?
Yes. Most work is an API service and a review screen added to your current web or mobile app. We read your code or API first to check where it fits. We first check how your app exposes data, then plan a small, safe integration.
Ready to scope it?
Tell us what you want to build. You get a fixed written quote after a free discovery call.