The problem is volume, not difficulty.
Most inbound support comes in across scattered channels (WhatsApp, email, phone, your platform), by text, voice note and photo. The questions look involved, but most follow a pattern and could be answered in seconds. Instead they sit in a queue.
The volume pulls skilled people away from the work only they can do. First replies take a day or more, worse in peak season. Customers chase across channels and get asked for the same thing twice. The slowest businesses simply lose them to whoever answered first.
A reply inside 5 minutes reaches and qualifies a lead at a rate that collapses within the hour.
Sources: MIT / InsideSales 15,000-lead study; Lead Connect, 2020.
Four things it does.
None of it is magic. Each is a specific task your team does by hand today, done the same way, at any hour, and handed straight back the moment a case needs a person.
Answers from your real knowledge base
It replies using your own criteria, not generic guesses. The source of truth stays in the model's working context, so answers are consistent instead of drifting over time.
Starts the paperwork
When a customer is ready, it identifies them against your platform and opens the request directly through your API, capturing the details you need to act. No re-keying, no dropped context.
Understands more than text
Customers send photos and voice notes, not tidy paragraphs. It reads images, transcribes audio, and groups rapid-fire messages into one coherent turn instead of replying to fragments.
Knows when to call a human
When a case needs a person, it escalates into your team's tools with the full conversation attached. The human takes over, the customer never notices a seam, and the assistant resumes with context intact.
It runs in two layers.
A messaging middleware sits at the front and handles everything that isn't conversation. Behind it, an autonomous agent runs the dialogue and decides what each message needs.
Messaging middleware
Layer 01 · the frontConversational agent · multi-model
Layer 02 · the dialogueThe route a message takes
Seven stationsWe're not tied to one model. We use Claude, Gemini and ChatGPT, and pick the right one for each job. When a conversation is dense with conditions and exceptions, choosing the right model is what keeps answers correct instead of plausible-sounding. We use several because it keeps the answers right, not because a longer list of model names looks impressive.
If the agent doesn't respond in time, the customer gets an apology and the case is escalated to a person automatically. Network calls retry on transient failures.
The point was never a clever demo. It's something that holds up on a Monday morning, with real customers waiting.
What it handles, and what goes to a person.
The important property isn't that it answers everything. It's that it knows the difference and escalates rather than guessing.
Move the sliders to see your own numbers.
Set your monthly volume and current response time. The estimate uses published industry benchmarks, not our own field data.
The repetitive, pattern-following share. A knowledge-base assistant resolves 55–70% of queries autonomously. We model a conservative 65%.
Cost assumes a blended €4.00 per handled contact vs €0.30 automated, at ~6 minutes each.
Where leads are 21× more likely to qualify and 100× more likely to be reached than at 30 minutes.
Benchmarks: Gartner Customer Service Technology Survey 2025, Forrester TEI of AI Customer Service 2025, Zendesk CX Trends 2026, Salesforce State of Service 2025, Meta / Juniper Research 2026. Figures are industry ranges, not Fixlab's measured results. Those are still being gathered.
We agree the test before we ship.
A conversational system is easy to demo and hard to trust. So we don't ship and hope. Before launch, we agree a written acceptance test with you: a golden dataset of real scenarios covering every conversation path: identification, knowledge questions, request creation, escalation, and resolution.
The bar is explicit: zero critical failures and a pass rate you sign off on. It goes to production only after it clears that bar, and after the full cycle, escalation to a human and back, is validated end to end. That's how you turn “it seems to work” into “it passed.”
We built this for Fixlab, and it's live with their customers.
Fixlab is a vehicle-certification business in Spain whose support was scattered across WhatsApp, email, phone and their platform. The assistant answers from their real knowledge base, files reform requests through their platform, and hands off to a person when a case needs judgment. It cleared the acceptance test we agreed up front, and it's in production.