Qantara TechnologiesQantara Technologies

Services

AI Development

Useful AI work starts with a business case, not a model. We look for the tasks where volume, repetition, and unstructured text are costing your team hours, document handling, triage, first-line support, forecasting, and assess which of them a system can take on with acceptable accuracy.

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What we build is wired into real workflows: an assistant your staff use inside the tools they already have, an extraction pipeline feeding your existing system, a forecast that reaches the person making the decision. Evaluation, human review, and guardrails are part of the build, not an afterthought.

Evaluation run
Groundedness62
Latency budget48
Cost per task71

evaluated against a fixed test set before release

Scored against a fixed test set before anything ships.

What you get

Deliverables

  • AI opportunity assessment
  • AI assistants and chatbots
  • Process and document automation
  • Machine-learning models
  • Predictive analytics
  • Data pipelines
  • Dashboards and reporting
  • Evaluation harnesses and guardrails
Analytics

Throughput

51/s

24h7d30d
Cost and latency watched in production.

How it works

A clear delivery process.

  1. 01

    Discover

    A session on the workflows, data, and decisions where AI could pay back.

  2. 02

    Scope

    A written proposal: use case, success measures, timeline, price.

  3. 03

    Design

    Workflow and interface design, including where humans stay in the loop.

  4. 04

    Build

    Iterative build with evaluation runs against your own data.

  5. 05

    Verify

    Accuracy, safety, and privacy review before anything reaches users.

  6. 06

    Run

    Monitoring, retraining or prompt maintenance, and an agreed support model.

Technology we use

  • OpenAI API
  • Anthropic API
  • Python
  • LangChain
  • Vector databases
  • Power BI
  • Looker

Questions

Frequently asked

Usually in high-volume, low-variance work that people currently do by reading: classifying incoming documents, extracting fields, drafting routine replies, summarising long records, answering repeated internal questions. These tasks have a measurable current cost and a tolerable error profile because a human still approves the output. We assess candidates with you during discovery and recommend starting with one where the payback can be measured, rather than a broad rollout.

Data handling is agreed in writing before any of it moves. We work with the minimum data needed, keep it within agreed regions and systems, and use commercial API terms that exclude your content from provider training where the provider offers that. Access is restricted and logged, and confidentiality is contractual. If a use case would require sending sensitive data somewhere you are not comfortable with, we design around it or tell you it is not worth doing.

Buy when a mature product already covers the workflow and your requirements are ordinary; the total cost is almost always lower. Build when the value comes from your own data, your own process, or an integration no vendor offers. Frequently the answer is a mixture: licensed models and platforms underneath, a thin custom layer where your business is genuinely different. We make that recommendation during the assessment, including when it means selling you less work.

With a test set drawn from your own data and reviewed by someone who knows the domain. We agree what counts as a correct output, measure against that set before release, and keep the harness so changes can be re-measured rather than eyeballed. In production we monitor outputs, log failures, and keep human review on the paths where a mistake matters. Where a system cannot reach a usable standard, we report it plainly instead of shipping it.

Industries this serves

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