Workflow and context
Map the real task, source information, permissions and the human decision the AI should support.
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AI integration
Put AI inside a useful workflow, with a plan for when it is wrong.
We integrate language and voice models into products and operations where they can remove work or improve a decision. The model is one component. Context, evaluation, cost and human control make the feature dependable enough to use.
Discuss your AI workflowA good fit
When an AI integration makes sense
- People repeatedly read, classify, summarize or draft from a known set of business information.
- A voice or chat interaction can complete a clear task and transfer exceptions to a person.
- The product can measure a useful result rather than counting generated words or chatbot messages.
What the work can include
We start with one workflow and define what a useful answer, a wrong answer and a safe fallback look like.
Model and cost choices
Route work to open-source or frontier models according to volume, quality needs and budget.
Evaluation and failure handling
Test representative cases, record weak patterns and define what happens when confidence or evidence is insufficient.
Product integration
Return structured results, connect approved actions and make escalation visible to the people responsible.
From demo to dependable feature
- 1
Choose one valuable task
A narrow workflow gives the team something real to evaluate instead of a general chatbot promise.
- 2
Build a representative test set
Useful examples include normal requests, unclear inputs and the cases where a wrong answer matters.
- 3
Integrate the controls
Add cost limits, source context, structured output, logging and human escalation where needed.
- 4
Review production evidence
Measure usefulness, corrections, failure patterns and cost before expanding the automation.
Related work
AI integrations already in use
Client work and company-owned products are labeled separately because both show capability but they are not the same kind of endorsement.
Topchecks financial reporting, client delivery
A separate AI processing module uses lower-cost open-source models for large summary batches and a frontier model for the final Romanian report and structured product output.
- Model routing keeps bulk work affordable
- Company history supports a reliability view
- The product receives structured results rather than pasted prose
Voice and workout products
Client voice agents use company-specific information and Retell AI phone flows. FitProgress, a company-owned app, creates personalized workout plans from its exercise library.
- Chat, voice and phone flows with human transfer
- FitProgress is our own product, not a client endorsement
- WhatsApp customer support remains work in progress
Responsible limits
AI output can be fluent and still be wrong. The workflow must decide what the model may suggest, what it may do and what needs a person.
- We do not present probabilistic output as guaranteed fact.
- Sensitive actions need appropriate validation, permissions and human control.
- A more expensive model is not automatically the right answer for every step.
- Production logging should capture operational signals without sending private enquiry text or unnecessary personal data to analytics.
Questions teams usually ask
Which AI model do you use?
The workflow decides. We compare quality, latency, privacy needs, volume and cost, and can combine open-source and frontier models when that is useful.
How do you test whether the AI is good enough?
We use representative examples and explicit acceptance criteria for the task. Evaluation includes unclear input and important failure cases, not only the happy path.
Can the AI take actions automatically?
Only where the business has defined approved actions and safeguards. Higher-risk or unclear cases should be escalated to a person.
Can you integrate with our existing application?
Yes. The AI layer can return structured results or call approved application functions, depending on the current architecture and permissions.
How do we control ongoing cost?
We measure usage by workflow, choose an appropriate model for each step, batch work where sensible and keep limits visible.
Start with the workflow, not the model.
Tell us what people do today, what information they use and what a useful result would change.
Discuss your AI workflow