/that users trust
We add language model features to your SaaS that solve a real step in your customer's work. Each one ships with test sets, usage limits and a clear record of what data goes to which model.
↓Each loop ships a different AI feature: the button in your app, the test set it must pass, the cost cap, then the data note
Heard yourself say this?Count how many fit.
Five things owners tell us before a project like this starts. Every one you recognise adds a tally mark.
Tap the lines you have said this year.
One fits. Worth a short call to see if AI features is the right fix.
Two fit. A short call will tell you whether to do this now or later.
Three fit. This is probably worth planning soon.
Four fit. You are losing time to this already.
All five. This page was written for you.
AI features,explained without jargon.
What does an LLM feature inside a SaaS product look like?
One idea at a time. Each sheet lays over the last, so the picture builds up. Tap a number to jump.
It is like giving each of your users a quick junior assistant who drafts the work and always hands it back for a final check.
It is a button, field or panel in your app that uses a large language model to do part of a task for the user.
Examples include drafting a reply, pulling fields out of an uploaded PDF, summarising a long thread or answering questions from the customer's own records.
Behind it sits a prompt, the right slice of the customer's data and checks on the output before it reaches the screen.
A good feature is narrow, measurable and easy for the user to correct.
Six parts,all handed over to you.
Your AI features work is made of these six parts. The comb lights each one in turn; tap a cell to read it.
Use case and success measure
We pick one task where AI saves your user real effort and agree how to judge it, such as fields extracted correctly or drafts accepted with light edits. Weak candidates get dropped early.
Retrieval over each customer's data
Where answers need the customer's own content, we build retrieval (RAG) with embeddings in Postgres via pgvector or a managed store. Every lookup is filtered by workspace so one tenant's data never feeds another's answer.
Evaluation suite
A set of real, anonymised test cases with expected outcomes, run automatically when prompts or models change. You see whether a change made things better or worse before users do.
Product interface for AI output
Streaming responses, visible sources, confidence cues and easy edit, retry and undo controls. Users always stay in charge of what gets saved or sent.
Cost controls per tenant
Token usage and cost per call are logged per workspace, with caps tied to plans. Cheaper models handle simple steps, and caching cuts repeat calls.
Data handling note
A plain document listing which fields go to which provider, retention settings and training opt-outs. Your team can use it for customer security questionnaires and DPDP Act reviews with your adviser.
- Use case and success measure
- Retrieval over each customer's data
- Evaluation suite
- Product interface for AI output
- Cost controls per tenant
- Data handling note
Picture a business like yours,in three frames.
A typical situation, not a client story. The line in each frame goes from tangled to straight as the work is done.
A Gurugram procurement SaaS adding contract review
Four legs of one relay,about 8 weeks, start to finish.
Each leg is sized by how long it usually takes. Watch the baton go round, or tap a leg to jump there.
Four factors,four corners of your project.
Each corner grows when that factor is on the larger side. The outline is your project's footprint, and the quote follows it.
A compact project. The smallest footprint for your quote.
Mostly compact, with one area that needs more work.
A balanced build. Half the factors on each side.
A larger build. Most areas need extra work and time.
The full footprint. Every factor on the larger side.
The question we hear most:Which model setup should your feature use?
The board turns through each option. Tap one to hold it and read the trade-off.
The task needs strong reasoning or writing and volume is modest.
Cost per call is higher and you depend on one provider's pricing and uptime.
Most requests are simple and only some need a stronger model.
More moving parts to test, and evals must cover each route.
Data must stay in your own cloud account or volume is very high and steady.
You take on GPU hosting, scaling and model updates yourself.
Four things that trip a project,and the fix we fit instead.
Each fuse is a mistake we see often. Watch it blow and get replaced, or tap a fuse to read the fix.
Shipping after testing on a handful of hand-picked examples
Build an eval set from real, varied cases and run it on every prompt or model change.
Sending whole customer records to the model
Send only the fields the task needs and strip identifiers where you can.
Offering unlimited AI use on a flat plan
Track cost per workspace and set fair-use limits or credits that match your pricing.
Letting the AI act without a review step
Keep a human confirmation for anything that sends, pays, deletes or commits on the user's behalf.
AI features questions,answered plainly.
Search the answers. If yours is missing, ask us on the call.
Cost & timeHow much will the AI feature cost us to run?
Running cost depends on how many calls users make, how much text each call carries and which model handles it. We measure cost per call during the prototype and show you a monthly estimate at expected usage before you commit. Caps per plan stop surprises.
Cost & timeHow long does one AI feature take?
A focused feature such as drafting or extraction usually takes 3 to 6 weeks including evals. Features that search across customer documents or call tools take longer. We agree scope per feature before starting.
Getting startedWill the model provider train on our customers' data?
The major API providers offer business terms that exclude API data from training by default. Settings and terms do change, so we configure the strictest options available and record them in your data handling note. Confirm the terms that apply to you with your adviser.
In IndiaHow do we answer DPDP Act and GDPR questions about AI?
We document what personal data each feature uses, the purpose, where it is processed and how long it is kept. That record helps you update your privacy notice and answer customer questions, and your adviser can confirm what notices or agreements you need.
OwnershipWho owns the prompts, evals and code?
Your company owns all of it, including prompts, test sets, retrieval code and configuration. Everything lives in your repository and your model provider accounts are opened in your company's name.
Getting startedWhat if a better or cheaper model comes out?
We keep the model behind a small adapter so switching providers is a configuration change. The eval suite tells you whether the new model actually performs as well on your tasks.
In IndiaCan the AI feature work in Hindi or other Indian languages?
Current models handle Hindi and several Indian languages reasonably, though quality varies by language and task. We include those languages in the eval set so you know how it performs before release.
Getting startedWhat happens after release?
We review logs, user corrections and costs regularly, and add failing cases to the eval set. A monthly plan covers prompt tuning, model upgrades and new AI features as your roadmap grows.
Works well alongside,from the same industry kit.
Add AI your customers will use with costs and quality you can see
Tell us the task your users find slowest and we will suggest whether AI can help, honestly, in our first reply.