No room to experiment
Your customers are asking for AI features and your roadmap has no room to experiment blindly.
AI Product Development
AI Product Development
We build AI products end to end, and we add AI features to products that already exist. That covers architecture, choosing models, designing the agent and prompt layer, evaluation, controlling running costs, and all the ordinary product engineering around it. We've done this for clients and for our own product, VoiceCite.
In practice01 02 03 04
Does this sound familiar?
Competitor shipped first
A competitor shipped something and you need to understand what it would actually take to match it.
Demo-only prototype
You have an AI prototype that works in demos but nobody trusts it with paying users.
Inference eating margin
Inference costs are eating your margin and you do not know how to fix it.
Context
You already have a SaaS product
You have paying users and a roadmap, and AI is now on it. The work here is usually narrower than people expect: intelligent search across user content, drafting or summarising, classification or routing, or a copilot inside the interface your users already know.
The hard parts are rarely the model. They're keeping inference costs from eating your margin, keeping latency low enough that the feature feels good, and making the thing behave predictably enough to put in front of paying customers.
Context
You're building something new
You have an idea for an AI product and you want it built properly rather than prototyped and abandoned. We take it from architecture through to a working product with real users on it.
We'll also tell you if we think the idea doesn't need AI, or if the AI part is the easy bit and the real work is elsewhere. That conversation is free and it has saved people a lot of money.
Proof
VoiceCite, our own product
VoiceCite runs an eleven agent pipeline: three agents doing research, eight doing generation, producing and publishing complete articles without a person in the loop.
We pay for its inference. We get the support emails when it breaks. Everything we know about running LLM systems in production, we learned on our own budget rather than a client's.
What we learned the hard way
- Evaluation before launch matters more than model choice. A cheaper model with good retrieval beats an expensive model with none.
- Inference costs scale with users, not with your team. We model unit economics before shipping.
- Non-deterministic outputs need guardrails and human review paths, not just better prompts.
- The first version should do one thing well. Six AI features on day one is how MVPs die.
How we work01 02 03 04
How we work through it
04 stepsScope the feature
What it does on day one, what success looks like, what it costs per user.
Week 1Architecture and prototype
Model selection, API design, and a working slice on real data.
Weeks 2 to 4Evaluate and harden
Test sets, latency tuning, cost controls, error handling.
Weeks 4 to 6Ship to users
Rollout, monitoring, and iteration based on real usage.
Week 6+Investment
What shapes the cost
Adding a feature to a product that already exists is usually the smaller piece of work, and how small depends on how well the existing codebase is put together.
- Part 01
Cut the first version
Building something new is scoped against what it has to do on day one, not eventually. We push hard to cut the first version down.
- Part 02
Running costs at scale
Running costs matter more here than in most projects, because they scale with your users rather than sitting flat. We'll model those with you before you commit, not after launch.
- Part 03
Don't overbuy infrastructure
If someone quotes you a very large number for a first version, they are often selling custom model training or infrastructure that a product at that stage does not need.
Good fit matters
Who this isn't for
If you want a demo to raise funding but have no plan to ship to users, we are not the right fit. If you need internal ops automation, not a product feature, look at agents or operations automation instead.
Options
How the options compare
| Criteria | Production AI feature (us) | Prototype / demo build | Off-the-shelf AI widget |
|---|---|---|---|
| Upfront cost | Fixed, scoped to day-one feature | Lower, often not production-ready | Subscription |
| Fits your product UX | Built in | No | Limited |
| Cost control at scale | Designed in | Not considered | Their pricing model |
| Evaluation before launch | Yes | Rarely | N/A |
| You own the code | Yes | Sometimes | No |
Production AI feature (us)
Upfront cost
Fixed, scoped to day-one feature
Fits your product UX
Built in
Cost control at scale
Designed in
Evaluation before launch
Yes
You own the code
Yes
Prototype / demo build
Upfront cost
Lower, often not production-ready
Fits your product UX
No
Cost control at scale
Not considered
Evaluation before launch
Rarely
You own the code
Sometimes
Off-the-shelf AI widget
Upfront cost
Subscription
Fits your product UX
Limited
Cost control at scale
Their pricing model
Evaluation before launch
N/A
You own the code
No
FAQ
Frequently asked questions
KaziHasan Ali
Founder & Principal Engineer at YeasiTech. Builds production web, mobile and AI products, and writes from work shipped since 2018. More at kazihasanali.com.
Tell us what you're trying to build
Fifteen minutes, no deck. Bring us a workflow that's eating your team's time, or a product idea you want built, and we'll tell you what we think it takes, what it roughly costs, and whether it's worth doing at all. Sometimes the answer is no, and we'll say so.
+91 8910704554(WhatsApp available)
ask@yeasitech.comMon-Fri, 10AM-7PM IST (Overlaps with UK, EU, Middle East & Australia)