AI implementation, not AI theatre
We find where your operation quietly loses hours every week, then build and ship the agents that give them back. In production, measured against your numbers, in weeks — not quarters.
45 minutes. No slides. You leave with a prioritised list of what to automate first.
The real problem
Licences were approved. A few people use a chatbot. Someone ran a pilot last quarter. And still, not one agent is running a real process end to end — the same work gets done the same way it did two years ago.
The gap is never the model. It's the implementation.
The proof of concept impressed the steering committee and never touched a production system, a real dataset or a real user.
Seats were rolled out company-wide. Nobody redesigned the workflow underneath, so the tool became one more tab nobody opens.
IT says it's a business problem, the business says it's an IT problem, and the vendor says it's a change-management problem.
How it works
The same structure on every engagement, whether you have 30 employees or 3,000.
We sit with the teams doing the work, map the processes end to end and quantify where hours and margin actually leak. You get a prioritised backlog scored by impact, feasibility and risk.
We build the highest-value agent first and put it in front of real users on real data. Integrated with your stack, monitored, with evaluations and guardrails from day one — not after the incident.
Agents drift as your business changes. We monitor quality, cost per task and adoption, retune what degrades, and roll the next process onto the same foundation.
Why Bable
We are the team that builds it. The people in the diagnostic are the people writing the code.
Every engagement is measured by what runs in production at the end of it. Documents are a by-product, never the deliverable.
No pyramid staffing. The engineers who scoped your problem are the ones implementing it, with direct access all the way through.
Model choice is the last decision, not the first. We design around how your operation actually works, including the parts that shouldn't be automated.
Your stack, your cloud, your repositories. No black boxes and no dependency on us to keep the lights on.
| With Bable | Typical consultancy |
|---|---|
| First agent live in 4–6 weeks | Discovery phase billed for three months |
| Senior engineers on the build | Partners sell it, juniors deliver it |
| Measured on hours and cost per task | Measured on deliverables submitted |
| Your repos, your cloud, full hand-over | Proprietary platform and lock-in |
Use cases
Representative engagements. Yours will look different — that's what the diagnostic is for.
Agents that resolve tier-one tickets end to end against your knowledge base and order systems, and escalate cleanly with full context.
Typical: 40–60% of tier-one volume deflected
Lead enrichment, qualification and CRM hygiene handled automatically, so reps spend their week in conversations instead of data entry.
Typical: 6–8 hours returned per rep per week
One place to ask anything about policies, contracts and documentation, answering with citations and respecting existing permissions.
Typical: 70% fewer internal 'who knows about…' pings
Extraction, validation and routing of invoices, contracts and claims, with humans reviewing exceptions rather than every record.
Typical: 80% of documents cleared without review
Agents that cross-check systems that were never designed to talk to each other and flag discrepancies before they reach the close.
Typical: month-end close shortened by days
Review, test and migration agents wired into your CI, tuned to your codebase conventions instead of generic defaults.
Typical: 25–35% faster cycle time
Clients
The talent Bable brought onto our team stood out from day one, and it made a real difference to what we could ship.
They got the staffing right, our velocity went up, and communication was seamless from the very first day.
We had a developer contributing to production in under a week. Their vetting process saved us months.
Their process gave us real-time collaboration and a clear lift in productivity.
It was intuitive from the start — the team adopted it with barely any training.
Engagement models
Fixed scope, fixed price, no open-ended discovery. Every engagement starts with the diagnostic.
From $500
1–2 weeks · typically $500–$1,500
For teams that know AI should be doing something here, but can't yet say what, or in which order.
From $4,000
2–4 weeks · typically $4,000–$12,000 by complexity
We build, integrate and ship one agent, then hand it over running in your environment.
From $1,000/mo
Monthly · typically $1,000–$3,000/mo
For companies that want the agents already running to stay reliable, and the next small use case to keep arriving.
Ranges, not price tags. Every engagement is scoped on a call before anything is quoted — there is no self-serve checkout.
FAQ
If yours isn't here, ask it on the call.
The diagnostic takes one to two weeks. The first agent is typically in production between weeks four and six. We deliberately start with a process that is valuable but narrow, so the first result arrives before the enthusiasm runs out.
Everything runs in your infrastructure and your cloud accounts by default. We work under an NDA and a data processing agreement, apply least-privilege access, and can keep processing inside the EU or the US where required. We do not train models on your data and neither do the providers we configure.
Developers are excellent at building what you specify. The hard part of AI implementation is deciding what to build, knowing which problems current models genuinely solve, and designing evaluation and guardrails so the system stays reliable. We bring that judgement, then leave your team able to run it.
No, and waiting for it is the most common way to lose a year. The diagnostic assesses what your data can support today. Most first agents need far less structure than people assume, and the gaps we do find become concrete work items instead of a vague blocker.
Whichever fits the problem, your compliance constraints and your budget. We are not resellers and take no vendor commissions, so the recommendation changes as the models do. Cost per task is a design constraint from the first day.
Then we say so and tell you what is — usually a process or integration fix that costs far less. You keep the analysis either way. We would rather lose an implementation than ship something you don't need.
Next step
Forty-five minutes with a senior engineer. Bring one process that frustrates you. You'll leave knowing whether an agent can take it on, roughly what it costs, and what would have to be true first.
Prefer email? Write to anthony@getbable.com and we'll find a slot.