AI-Powered SaaS Products
SaaS products built with AI as a core part of the product experience, not a chatbot bolted onto the side, engineered to handle real usage, cost, and reliability at scale.
AI-Powered SaaS Products
- req/s, zero deadlocks
- 15Kreq/s, zero deadlocks
- double-charges in production
- 0double-charges in production
- production systems shipped
- 10+production systems shipped
- years building for clients
- 7+years building for clients
Signs you need this now.
Adding an AI feature to a SaaS product is easy to prototype and hard to productionize: costs spiral when usage grows, latency makes the feature feel broken, and outputs are inconsistent enough that users stop trusting them. Most teams can get a demo working with an API call, but building the product-grade version, with proper prompt architecture, cost controls, caching, and fallback handling, requires a different level of engineering.
The AI feature demos well but the API bill is climbing fast
Usage grew and so did the invoice, because every request re-sends full context with no caching or prompt optimization in place. Nobody budgeted for the AI costs to scale linearly with the user base, and now it's a line item finance is asking about.
Users have started ignoring the AI feature because it's unreliable
Sometimes the output is great, sometimes it's inconsistent or slightly wrong, and there's no evaluation process catching regressions before they ship. Once users get burned a few times, they stop trusting the feature even when it's working correctly.
It works in the demo, but nobody's tested it under real concurrent load
The prototype was built and tested by one person clicking through it, not the several hundred users hitting the API simultaneously that production will bring. There's no queueing, rate limiting, or graceful degradation plan for when the model provider is slow or the rate limit gets hit.
What you get.
Production-grade prompt and context architecture
Prompts engineered and version-controlled like code, with context management that sends only what's needed per request instead of ballooning token usage as your data grows.
Cost monitoring and control built in
Token usage tracked per feature and per user, with caching, batching, and model-tiering strategies applied so costs scale sub-linearly with usage instead of matching it one-to-one.
Reliability and fallback handling
Retry logic, timeout handling, and graceful degradation when a model provider is slow or rate-limited, so a provider hiccup doesn't take down the feature for every user at once.
Output evaluation and quality tracking
A lightweight evaluation process for testing prompt changes against known good/bad examples before they ship, so regressions get caught before users notice them.
Streaming and latency optimization
Responses streamed to the user as they generate rather than waiting for a full completion, so the feature feels responsive even when the underlying model call takes several seconds.
Usage-based architecture for your billing model
The AI feature is instrumented in a way that maps cleanly to your existing plans or usage-based billing, so you can price and gate it without hacking something together after launch.
Four steps, no mystery.
Quick scoping call
A short call (or async over WhatsApp) to understand what you're working with and what "done" actually looks like for you.
Fixed scope, no surprises
A clear written plan of what's included and how long it takes, before any work starts.
The actual work
Progress you can see, not a black box. You get updates as milestones land, not just a status report at the end.
Handover
Everything documented and handed over cleanly, with a walkthrough so your team isn't stuck waiting on me for routine changes.
You might also need.
Frequently asked.
Most commonly Claude, GPT, or Gemini, chosen based on the specific task, cost profile, and latency requirements rather than defaulting to one provider for everything.
Tell me what you're dealing with.
Send a message and get a real reply within 24 hours, not an automated sequence.
Or WhatsApp directly, same link as above