Chatbots
Work on simple, repeating flows. Extending them into complex service means maintaining intents, scripts, rules and exceptions — expensive to build, heavy to run, and capped in quality on anything non-standard.
AI operator for the contact center
Lower your cost to serve — on top of the automation you already have.
Extra automation on the hard parts of the work, at your operators' quality. Bitsu learns from how your operators actually work, then does the same work in the same interfaces.
- No prompts
- No APIs
- No process mining
- No perfect knowledge base
Book a 30-minute call
60% of contacts automated at your operators' level of quality — in production at a top-5 bank.
The simple half is automated. The cost didn't go away.
A chatbot resolves around half of incoming contacts. The rest still goes to operators, and every additional percentage point costs more than the one before it — while an operator costs $10–15k a year and a single chat costs $1–2.
RPA
Handles routine actions in stable interfaces, and breaks when a process or a screen changes. It needs process mining up front, and on rarer processes the build costs more than it saves.
LLM agents
Flexible on complex cases, but a demo launches fast and maintenance at scale doesn't. Instead of scripts you now maintain prompts, plus separate processes for quality assurance and change control.
Every one of them needs a human to hand-build the logic for each process — and rebuild it each time the process changes.
60%
automation, at your operators' level of quality
That is 60% of the contacts still reaching your operators after the chatbot has taken its share — handled end to end, without a person.
Measured in production at a top-5 bank, across teams of 3,000+ operators, by A/B test on live traffic. Not a lab pilot.
Quality does not pay for it
Two independent measures — customer CSAT, and blind internal review that scores dialogues without knowing whether AI was involved. In both domains, at or above what the bank's own people scored.
| Domain | Bitsu | People |
|---|---|---|
| Simple | 98 | 94 |
| Complex | 87 | 86 |
On complex, multi-workflow processes, Bitsu adds automation on top of chatbots, RPA and LLM agents already running — the work those approaches leave behind. Copilot alone starts returning operator time before anything runs autonomously at all.
Automation is not the hard part. Automation that holds quality is.
Three steps, and the work itself is the training data
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01
Observe
A plugin logs what operators do — what they saw, where they navigated, which data they checked, what they entered, what they told the customer. Their work does not change.
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02
Copilot
Bitsu proposes the next action; the operator confirms or corrects. Savings start here, and every correction trains it further. When the model gets something wrong, the operator teaches it the right action immediately, just by doing their job.
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03
Autopilot
Once quality is proven on a workflow, it runs on its own. Anything uncertain hands back to a person.
Autopilot is never switched on before copilot quality is proven.
We automate the simple parts of every process — not whole processes
How the market automates
The entire process
A process has to be large enough to pay back the build. It is automated end to end, or not at all. That is why the backlog never ends and why rare processes never get done.
How Bitsu works
The simple parts of any process
Bitsu learns an operator's job end to end, then takes over the parts it is confident about. Everything harder stays with the person. As it learns, the boundary moves.
No process mining. No analysis phase to decide what to automate first. Value starts small and compounds instead of arriving all at once at the end of a project — or not at all.
One conversation, moving freely between AI and person
Work starts with Bitsu. When it hits something it is unsure about, a person picks up mid-task — same screen, same context, nothing rebuilt. The moment it becomes routine again, Bitsu takes it back.
Because it works in the interfaces your operators already use, they can see exactly what it did. That is what makes their corrections usable: every confirmation, correction and manual takeover becomes training data. What the system cannot do today is tomorrow's training example.
Train it on your best operators — and raise the floor for everyone
Because Bitsu learns from real work, you choose whose work it learns from: the operators who are fastest, friendliest, most careful with detail, strictest about policy.
Top-performer standard, every time
Today the quality a customer gets depends on which operator picks up. With Bitsu it doesn't — at peak volume, overnight, and through turnover.
Your people get better too
Suggestions arriving next to an operator set the standard in real work: new hires reach competence faster, and quality variance across shifts falls.
Service quality gets more consistent. The savings come with it.
What we do that the alternatives don't
| Capability | Chatbots | RPA | LLM agentsprompts + API | Bitsu |
|---|---|---|---|---|
| Complex, multi-workflow processes | — | — | ◐ | ✓ |
| No manual maintenance of flows or prompts | — | ◐ | — | ✓ |
| Resilient to interface changes | ◐ | — | ◐ | ✓ |
| Works on top of systems already deployed | ◐ | ◐ | ◐ | ✓ |
| On-premise, inside your perimeter | ◐ | ✓ | ◐ | ✓ |
✓ supported ◐ partial — not suitable or hard to scale
The individual pieces exist elsewhere. Computer use is being built by the frontier labs. Demo-to-automation startups exist. There is a contact-center copilot vendor. RPA vendors have added action recording. Nobody combines them: behavioral cloning, so skill comes from real trajectories rather than written prompts; computer use, so people and AI continue each other's work in the same system; and selective automation, so value arrives in pieces instead of all at the end.
The four questions every buyer asks
- Quality
- You start in copilot, where the operator controls anything that matters. Autopilot is enabled gradually, per workflow, only after the metrics hold. Bitsu is reviewed by the same QA function that reviews your people.
- Data
- Trained and running on-premise, on your infrastructure. Data never leaves your perimeter.
- Integration
- We need access to the operator workstation — nothing else. No rebuilding internal systems, no API project across your backend. Bitsu works in the same interfaces a person does.
- Security
- There is an explicit list of actions Bitsu never takes on its own: it does the preparatory work and leaves the decision to a person. It runs with the same permissions as an operator — no access to internal databases or APIs — and those permissions can be narrowed further.
Where we fit best
We are a fit if you have:
- A large text-based contact center — chat or messaging
- 50+ operators doing the same kind of work, enough volume for the model to learn from
- Repetitive, multi-step workflows across internal systems
- A process owner and an existing QA framework — you already measure quality on people, and we want Bitsu measured the same way
We are not a fit yet if your priority is voice. Text is where the technology is proven.
Next step: a 30-minute call
We work as a design partnership, not a product purchase. Our team works alongside yours on one line: the model trains on your data, in your interfaces, inside your perimeter. You end up with a working system and people who can run it without us.
Early partners get terms that won't be available later — the first external cases matter more to us than margin.
Let's look at your line and size the potential together.