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Service 05

AI Solutions

Assistants, extraction and intelligent search built into products people already use — with the model kept away from anything that has to be exactly right.

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Scope

What is included

AI where it earns its place. Everything below is part of the engagement unless we agree to cut it.

Assistants grounded in your contentAnswers come from your documentation, not from the model imagining them.
Document and data extractionTurn invoices, forms and reports into structured data, with a human check on low-confidence results.
Intelligent searchSearch that understands intent rather than only matching keywords.
Workflow automationThe repetitive steps between systems, removed.
Human handoverA clear escalation to a person when the assistant is not confident.
GuardrailsRate limits, prompt-injection defences, logging, and calculations kept in tested code.

Technologies we use here

PythonNode.jsVector searchAWSEvaluation harness
How we work

The workflow, step by step

7 stages from first conversation to running system. Each one ends with something you can see and sign off, so progress is never a matter of trust.

  1. 01

    Find the honest use case

    We look for tasks that are repetitive, high-volume and tolerant of review. If AI is not the right tool we say so.

    You get: Use case, expected value, and the risks
  2. 02

    Prepare the knowledge

    Your content collected, cleaned and indexed so answers have somewhere true to come from.

    You get: Indexed knowledge base
  3. 03

    Prototype

    A narrow working version on real data, measured against real questions.

    You get: Prototype + accuracy measurement
  4. 04

    Add the guardrails

    Confidence thresholds, handover rules, injection defences, and hard limits on what it may act on.

    You get: Safety rules documented and tested
  5. 05

    Integrate

    Wired into the product, the site or the internal tool where the work actually happens.

    You get: Feature live behind a flag
  6. 06

    Evaluate

    Answers reviewed against a fixed question set before wider release.

    You get: Evaluation report
  7. 07

    Monitor and improve

    Real questions logged, gaps found, knowledge extended on a cycle.

    You get: Monthly review and updates
Questions

Before you ask

Will it make things up?

Not if it is built properly. We ground answers in your content, set a confidence threshold, and hand over to a person rather than guessing. Anything numeric is computed in tested code, never by the model — that is exactly how Invora handles totals.

Is our data used to train someone else’s model?

Not under the configurations we deploy. We confirm the data-handling terms of any provider in writing before we build on it.

Can it replace our support team?

No, and we would not sell it that way. It deflects repetitive questions so your team spends time on the ones that need a human.

Ready to start?

Tell us the goal and the deadline. You will get a scope, a timeline and a figure.

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