Shimira ITAI features for existing products
Add AI to the product you already run, without rebuilding it.
Your product works and has customers. Now every competitor has shipped an AI feature, your customers are asking, and your team is fully occupied keeping the roadmap moving.
You don't need a rebuild, and you don't need AI everywhere. You need one or two features that make your existing product measurably more useful, built into what you already run, by people who won't destabilise it. That's the work we do here.
What matters here
What usually earns its place
Summaries
Long records, threads, or histories condensed to what matters, so users stop scrolling to catch up.
Search that understands meaning
Users find what they meant rather than the exact words they typed. Often the highest-satisfaction feature per pound spent.
Drafting inside the product
First drafts of replies, descriptions, or reports, generated from the data your product already holds.
Classification and tagging
Records sorted, prioritised, or tagged automatically, cleaning up the manual admin your users complain about.
An assistant over their own data
Users ask questions about what's in their account and get cited answers, without exporting anything.
- Next.js
- React
- Node.js
- TypeScript
- PostgreSQL
- OpenAI
We've shipped in this space
Eyeon Portal
A live platform we extended and still run: the discipline of adding capability to software real customers depend on, without breaking what already works.
What goes wrong
Adding AI to a live product: what to watch
Destabilising what already works
The first rule is do no harm. AI features ship behind flags, degrade gracefully when the model is slow or unavailable, and never block a core flow.
Unbounded running costs
A feature every user can call repeatedly can quietly cost more than the subscription it sits inside. Usage limits, caching, and model routing belong in the first version, not the second.
Customer data going somewhere new
Sending customer data to a model provider is a change your terms, and possibly your contracts, need to reflect. Enterprise customers will ask, so decide the answer before they do.
AI for its own sake
A feature nobody uses costs you engineering time and running cost forever. We start from the job users are struggling with, not from the technology.
No way to know if it worked
Usage, accuracy, and cost per user should be measured from day one, so you can tell whether to invest further or switch it off.
How it runs
How we work alongside your team
- 01
Pick the feature
1 weekWe look at where users struggle and what data you already hold, then recommend the one or two features with the best ratio of value to risk.
- 02
Feasibility and cost model
1–2 weeksTested on your real data, with a per-user running cost you can compare against your pricing before committing.
- 03
Build behind a flag
4–8 weeksBuilt in your codebase, to your conventions, shipped to a subset of users first. Your team reviews every pull request.
- 04
Measure, then widen
ongoingUsage, accuracy, and cost watched on real users before the feature goes to everyone.
Timelines & pricing
Honest ranges, before you commit.
| Tier | What's included | Timeline | Typical cost |
|---|---|---|---|
| Feasibility assessment | Feature selection, tested on your data, per-user running cost modelled | 1–2 weeks | $3,000 – $8,000 |
| One AI feature | Built into your product, behind a flag, with usage limits and monitoring | 4–8 weeks | $15,000 – $45,000 |
| AI across the product | Several features, shared infrastructure, evaluation harness, cost controls | 2–4 months | $30,000 – $90,000 |
Ongoing AI cost should be modelled against your pricing before you build, especially on flat-rate plans. Our AI app development cost guide shows the calculation and the levers that move it most.
Before you choose
Ready to add AI to your product?
- There's a specific job your users find slow or tedious today.
- Your product already holds the data the feature would need.
- You can say what a good output looks like, so it can be measured.
- You know whether your terms and contracts allow sending customer data to a model provider.
- You can absorb a per-user running cost, or you'll price the feature accordingly.
- Someone on your side can review our pull requests.
Missing a few? The feasibility assessment is designed to settle exactly these before you spend a build budget.
How you work with us
Three ways to engage.
Fixed price
Prototypes from $7,500
Production MVPs from $25,000
You know what you need built. We scope it, quote one price, and deliver against it, no meter running.
- One agreed price, agreed before we start
- Milestone-based payments tied to what you can see
- Change requests priced openly, never assumed
Time & materials
from $28 / hour
Blended ~$32; senior specialists to ~$45
For work that changes as you learn. You pay for the hours spent, see exactly where they go, and can steer week to week.
- Billed on real, logged hours, reviewed with you
- Reprioritise or change direction any sprint
- Start small, scale the team as it proves out
Dedicated team
from $2,800 / month
Per developer, full-time, see the rate card
Developers who work only on your product, full-time, as an extension of your team, the offshore model most of our long-term clients settle into.
- The same people, month after month
- Your tools, your standups, your roadmap
- Roughly half the loaded cost of a local hire
Full rate card and what's included on the pricing page. Or hire AI engineers into your own team instead.
Questions
Before you get in touch.
Yes. We work in your repository, follow your conventions, and open pull requests your team reviews. This is an extension of your product, not a parallel system, and we're used to working alongside an in-house team.
We're fastest in TypeScript and Python, and we'll say honestly on the first call if your stack is outside what we work in daily rather than learning it at your expense.
Model it before building, then control it: route easy requests to cheaper models, cache repeated ones, keep prompts short through better retrieval, and set per-user limits. On flat-rate plans this matters enormously, and we design for it from the start.
Not on the enterprise API tiers we build on. Where data can't leave your environment at all, open-weight models inside your own infrastructure are the alternative. Either way it's agreed in writing before any build.
A feasibility assessment is $3,000–$8,000. One AI feature built into an existing product runs $15,000–$45,000, and a broader rollout $30,000–$90,000, plus a running cost per user.
That's why it ships behind a flag to a subset of users, with usage and accuracy measured. Switching it off is cheap and planned for. Finding out after a full rollout is the expensive version.
Cost guides
Budgeting for this?
Real 2026 numbers, worked examples, and the hidden costs most quotes leave out.
How much does AI app development cost in 2026?
Build ranges by use case, a worked monthly model-cost example, RAG vs. fine-tuning, ROI maths, and the hidden costs most AI quotes skip.
Read the guide →How much does software maintenance cost in 2026?
The 15–25% rule with worked examples, a maintenance calendar, typical monthly plans, a contract checklist, and what happens to software nobody maintains.
Read the guide →Not sure what you need yet? That's the usual starting point.
Tell us the problem in your own words. We'll scope it with you and put the plan in writing, free, and yours to keep either way.