/users actually use
We design and build LLM features inside your existing app, from the prompt to the UI to the monitoring. Each feature has tests, a usage budget and a switch to turn it off without a release.
Evaluation suite40 real tickets, graded answers
↓An AI button inside your product, a test suite that grades every prompt change, then a careful rollout with a cost cap.
AI in your product,explained without jargon.
What does adding AI to a product involve?
One idea at a time. Each sheet lays over the last, so the picture builds up. Tap a number to jump.
It is like adding a skilled contractor to your team who works through a ticket system: you write clear briefs, check the work, and cap their hours.
Your app sends a carefully written request to a large language model through an API and shows the result to the user.
Around that call sits most of the real work: choosing the model, giving it the right data, checking the output, handling failures and controlling cost.
Good AI features feel like normal parts of the product, such as a summarise button or a smarter search box.
They also need a way to measure whether answers are good.
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 Bengaluru HR software company adding interview summaries
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 in your product 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.
Six parts,all handed over to you.
Your AI in your product work is made of these six parts. The comb lights each one in turn; tap a cell to read it.
Feature specification
A short document covering the user problem, inputs, expected outputs, failure cases and how success will be measured.
Model and prompt layer
A clean service in your codebase that wraps the model calls, with versioned prompts and the ability to switch providers.
Evaluation suite
A set of test inputs with graded expected outputs that runs in CI, so prompt or model changes cannot quietly make results worse.
Guardrails
Input checks against prompt injection, tenant isolation so data never crosses between customers, and output checks before anything is shown.
Cost and latency controls
Per-user and per-account limits, caching for repeated requests, streaming responses and alerts when spend crosses a threshold.
Observability dashboard
Tracing of every AI call with tokens, latency, cost and user feedback, using tools such as Langfuse or your existing monitoring.
- Feature specification
- Model and prompt layer
- Evaluation suite
- Guardrails
- Cost and latency controls
- Observability dashboard
The question we hear most:Hosted model API or self-hosted open model?
The board turns through each option. Tap one to hold it and read the trade-off.
You want the strongest quality quickly and your data terms allow sending content to a provider under business API agreements.
You pay per token and depend on the provider's pricing and model retirements.
Data must stay within your own infrastructure or you have very high, predictable volume of a narrow task.
GPU hosting and model maintenance add effort, and quality on complex tasks may trail the top hosted models.
Simple high-volume tasks like classification can run on a small model while harder reasoning goes to a hosted one.
Two setups mean more moving parts to monitor.
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 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 without an evaluation set
Without known-good test cases you cannot tell if a model update helped or hurt, so we build evals first.
Putting all customers' data into one search index without filters
Every retrieval query is scoped by tenant at the database level, which we test explicitly.
Hard-coding one provider everywhere
A thin model layer lets you switch or mix providers when prices or quality change.
No spending ceiling per account
One heavy user or a bot can run up large bills, so per-account limits ship with the feature.
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.
AI in your product questions,answered plainly.
Search the answers. If yours is missing, ask us on the call.
Cost & timeHow long does it take to ship an AI feature?
A focused feature such as summarising a record usually reaches a small group of users in three to four weeks. Features that search large data sets or take actions take longer because of the testing involved. The prototype in week one tells you early whether the idea is worth building.
Cost & timeWhat does it cost to run?
Running cost is mostly the per-token price from the model provider, which depends on how often the feature is used and how much text it reads. We estimate it per active user before the build and add caching and limits to keep it predictable.
OwnershipWho owns the code and prompts?
Everything is written in your repository and belongs to you, including prompts, evals and infrastructure code. Provider accounts are opened in your company's name.
Getting startedCan it work with our existing stack?
Yes. We work in common stacks such as Node.js, Python, Laravel, React and Flutter, and follow your code review and deployment process. We adapt to your conventions.
In IndiaWhat about data protection for Indian users?
We send the model only the fields a feature needs, mask personal identifiers where possible and record what data is processed. If your customers include regulated businesses, you may need data residency or self-hosting. Check with your adviser on DPDP Act duties.
Getting startedHow do you stop prompt injection?
We treat user-supplied text as untrusted, separate it clearly from instructions, limit which tools the model can call and validate outputs before acting on them. No defence is complete, so actions with real consequences require user confirmation.
Getting startedWhat happens when a model is retired?
Providers retire older models on published schedules. Because the evals run automatically, we can test a replacement model against your cases and switch with evidence that quality holds.
After launchDo you support the feature after launch?
Yes, on a monthly plan or through a dedicated developer. That covers prompt tuning from user feedback, model upgrades, cost reviews and fixing edge cases found in production.
Works well alongside,from the same practice.
Give users AI that holds up in production
Share the feature idea and a sample of real data, and we will prototype the hard part first.