Your AI product should get better with every user.
CLaaS connects the data you already have (product events, agent traces, your warehouse) and learns what every user and account keeps correcting. Then it feeds that back into your product, whether that's your agents, your UI or your APIs, and shows you, in numbers, that it's improving.
You keep your current model, and you don't need an ML team or fine-tuning.
You shipped AI. Now it's stuck.
Most teams already collect the right signals: edits, retries, rejections, thumbs-down. Those signals land in your analytics and tracing tools and never make it back into the product. So the 1,000th session is no smarter than the first.
Adoption is flat.
You launched the copilot and usage hasn't grown since.
Users keep fixing the same things.
The same edits, account after account, week after week.
Customers say the AI isn't useful.
You see it at renewal.
You can't prove it's getting better.
When the CEO or board asks, there's no number to show.
Connect it once, and it keeps learning.
Read-only connections to your product analytics, LLM traces and warehouse. Nothing in your product changes.
CLaaS finds the corrections each user and account keeps repeating, and keeps what it learns current as your product, users and models change.
Your agents, UI and APIs read what CLaaS has learned at runtime over MCP or an API. A dashboard shows whether the product is actually improving, measured against a holdout group.
It keeps running without a maintainer.
Connects to your stack
Read-only connectors for your product analytics, LLM tracing and warehouse.
Learns your domain
We set up what a correction and a success mean in your product once. After that, it learns from your data.
Always running
Old corrections expire and regressions get flagged. It keeps working when your engineer moves on.
Measured like uptime
Track how fast your product learns: repeat corrections, time-to-value, improvement by account.
Works with your model
No fine-tuning and no model hosting. Keep your current provider. The same corrections and preferences can feed agents, UI defaults and API logic.
You own the data
Inspect, edit or delete anything it has learned. They can live in your own warehouse.
If session 50 isn't better than session 1, your product isn't learning.
Every engagement starts with a baseline of five numbers.
- Repeat-correction rate
- How often users fix something they've already fixed before.
- Feedback coverage
- How many sessions produce a usable feedback signal.
- Signal-to-change time
- How long it takes for a recurring problem to change the product's behavior.
- Time to value
- How fast new users get to their first success.
- Improvement over time
- Whether outcomes improve the longer a user or account uses the product.
I built these loops in-house. The first version took a few weeks. Keeping it working took part of every sprint after that.
Every new feature, prompt change, model upgrade and customer adds signals to capture and corrections to keep current. In-house, that work loses the priority fight, depends on one person, and rarely comes with proof that it works.
Your engineers stay on your product
Your best AI engineers can spend their sprints on what makes your product different, and leave the feedback pipeline to us.
Know when it stops
Nothing alerts you when a product stops learning.
We're recruiting three design partners.
An 8-week engagement, built together with one engineer on your side. Free for design partners.
Read-only access, the five metrics, and your top three recurring corrections, with real examples. Together we pick one to fix. Setup takes a few hours of your engineer's time in total.
We build, your engineer tests it in your product, and we meet weekly.
Did the number move against the baseline?
Good fit if you
- Have AI features in production with weekly users
- Already run product analytics and LLM tracing
- Have no dedicated ML team
- Would rather not hire one for this
- Can spare one engineer for about an hour a week, plus a 30-minute weekly call
You get
- The fix built for free
- Founding pricing if you continue
- A direct line to the founder
Built by an engineer who's done this in-house.
I'm Tejas. I've spent my career owning critical backend systems: distributed systems at Egencia (Expedia's corporate travel business), engineer #4 at Courier (YC S19), and CTO and co-founder of Swayware. Most recently I've been the engineer responsible for building AI learning loops in-house. I saw how much ongoing work these loops take. CLaaS does that work so your team doesn't have to.
Questions
No. CLaaS learns at the context and memory level and serves what it has learned to your agents at runtime. It works with whatever model provider you use today.
Read-only access to your product analytics, LLM traces and warehouse, plus one engineer: a few hours for setup in the first two weeks, then about an hour a week and a 30-minute weekly call.
The learned data can stay in your own warehouse. You can inspect, edit and delete anything CLaaS has learned.
Not for the baseline. Later, your agents, UI or backend read what CLaaS has learned over MCP or an API. You choose where it's used.
We're starting with common stacks: product analytics like PostHog, Segment or Amplitude; LLM tracing like Langfuse, Braintrust or LangSmith; and warehouses like BigQuery, Snowflake or Postgres. Tell us yours.
Free for design partners. Pricing for everyone else comes later.
Is your AI product learning?
Tell us a bit about your product. If it's a fit, we'll set up a 30-minute call.