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HyperzenHyperzen

Applied intelligence, not experiments

AI that earns its place in your product

From first use case to production system — model selection, evaluation, guardrails and the engineering around them.

The problem

Most AI work stalls at the demo

A prototype impresses in a meeting and then collapses on contact with real data, real permissions and real volume. The gap is rarely the model. It is retrieval quality, evaluation, latency budgets, cost control and the plumbing that connects intelligence to the systems people already use.

Our approach

Systems engineered around the model

We start from the decision the AI is meant to improve, then build backwards: data access, retrieval, prompt and model strategy, evaluation harness, human review paths and observability. The result is a capability your team can trust, measure and keep improving after we hand it over.

Capabilities

What the engagement includes

  • Use-case discovery

    Identify where intelligence changes an outcome, and where conventional software is simply better.

  • Retrieval architecture

    Grounded answers from your own documents, databases and systems of record.

  • Evaluation harness

    Repeatable scoring so quality is proven before every release, not assumed.

  • Guardrails & review

    Policy checks, escalation paths and human-in-the-loop where stakes are high.

  • Cost & latency control

    Caching, routing and model tiering that keep unit economics sane at scale.

  • Observability

    Traces, feedback capture and dashboards that show exactly how the system behaves.

Also covered

  • Retrieval-augmented generation over private knowledge
  • Document understanding and structured extraction
  • Classification, routing and triage
  • Conversational assistants with tool access
  • Forecasting and anomaly detection
  • Recommendation and ranking systems

Process

How delivery runs

Each phase ends with something you can see, use and judge.

  1. 01

    Frame

    Pin down the decision, the data available and how success will be measured.

  2. 02

    Prototype

    A working slice against real data within weeks, not quarters.

  3. 03

    Evaluate

    Build the scoring set, measure quality, cost and latency honestly.

  4. 04

    Productionise

    Harden, integrate, monitor and hand over with documentation and training.

Technology

What we build with

  • Python
  • TypeScript
  • PyTorch
  • LangChain
  • pgvector
  • Postgres
  • Redis
  • Docker
  • Kubernetes
  • AWS
  • Azure

Outcomes

What you get

  • Decisions supported by evidence rather than intuition
  • Hours of manual review compressed into seconds
  • Quality that can be measured and defended
  • A platform your own engineers can extend

Use cases

Where this applies

  • 01

    Knowledge assistant

    Answer internal questions from policy, product and process documentation with citations.

  • 02

    Document processing

    Turn invoices, contracts and forms into structured, validated records.

  • 03

    Support triage

    Classify, prioritise and route incoming requests with confidence scores.

FAQ

AI Solutions questions

Not usually. Most systems run on managed model APIs plus your existing database. Where data residency or cost demands it, we deploy open-weight models on infrastructure you control.

Related

Other services

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