AI & automation
An assistant that answers 80% of enquiries well and hands the rest over with context already saves money. One that invents answers destroys trust in a week. We work against agreed acceptance metrics before going live.
Applications
01
The assistant answers frequent questions, qualifies leads and books appointments, handing over to a person when the conversation requires it.
02
Document reading, payment reconciliation, report generation and classification of repetitive cases.
03
Measure which action produced which result, with opaque identifiers, signed postback and conversions verified against the network.
04
Natural-language queries over your own database, with role-based permissions and answers that cite the source record.
Criteria
| Criterion | Generic automation | TYKUN |
|---|---|---|
| Data | Trained or queried on external services | Runs on your infrastructure or the one we agree |
| Traceability | No record of why it answered | Record of answer, source and hand-off |
| Evaluation | Validated with demonstrations | Test case set and a signed acceptance metric |
| Cost | Flat subscription per user | Measured usage, with limits and alerts |
| Exit | Tied to the model vendor | Interchangeable model; logic and data are yours |
Pilot
01
We pick two or three cases with real volume and define what working means: resolution rate, response time, complaints or sales.
02
We select and clean the source material: documents, policies, conversation history and authorised database access.
03
We build the flow, test it against the agreed case set and fix it before it talks to a real customer.
04
Gradual rollout, conversation review, quality alerts and a clear procedure to switch automation off if something fails.
We start with one: the one that consumes the most time and produces the most errors. The pilot tells you in weeks whether to scale it.
