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AI Lab

We treat AI as software: scoped to a business outcome, grounded in your own data, wired into the tools you already run, and evaluated before and after every change. This is where we show what that looks like in practice.
lab.consolepipeline

Lab index

7 items
  • Delivered1
  • Internal tool2
  • Prototype1
  • Concept3

Only Delivered and Internal tool entries are real builds. Prototypes and concepts are labelled so they are never mistaken for client work.

Philosophy

Practical, ROI-driven, and honest about what AI can do.

Three principles every build in the lab has to satisfy. They are the difference between a demo and something a business can rely on.

  1. 01

    Grounded in your data

    Models only see what you approve — your content, your records, your answers. An output that cannot point to its source does not ship.

  2. 02

    Integrated with your tools

    AI lives inside the systems you already run: CRM, booking, POS, support desk. Agents act through real APIs with a person approving anything irreversible.

  3. 03

    Evaluated continuously

    Golden datasets, written evaluation rules and regression runs on every prompt or model change. AI features are tested like software because they are software.

Showcase areas

Seven areas, one method.

Pick an area to see what is in the lab for it. Each one is built with the same grounding, integration and evaluation discipline.

In the lab

What we have built, what we run, what we are exploring.

Every item carries a status. Only Delivered and Internal tool entries are real builds; prototypes and concepts are labelled so they are never mistaken for client work.

LabelsDelivered= shipped for a clientInternal tool= used inside QualiteSoftPrototype= working build, not client workConcept= under exploration, not built

7 items shown

  • Generative AIDelivered

    Career Intelligence Engine

    Profile-grounded AI that reads patterns across a member's answers, proposes career paths with written reasons and evidences strengths — built for an AI career-coaching platform.

    • Generation receives only the member's stored answers; every insight must cite at least one.
    • Acceptance test: two different profiles must produce visibly different dashboards.
    • Language constrained to 'your responses suggest' — no psychological claims.

    Node.js · React · Azure · LLM APIs

    View the case study
  • AI AgentsInternal tool

    Outreach Research Agent

    An internal agent that researches prospects and markets from public sources, structures findings and drafts outreach for human review.

    • Tool-using agent with explicit steps: gather, verify, summarise, draft.
    • Human approval before anything is sent.
    • Details of the current internal implementation to be confirmed by QualiteSoft before launch.

    TypeScript · LLM APIs · Web search tools

  • AI/LLM TestingInternal tool

    AI-Assisted QA Tooling

    Evaluation harnesses and test-generation helpers the QA team uses to test AI features: golden datasets, LLM-as-judge scoring and prompt regression in CI.

    • Golden-set evaluations for accuracy and hallucination checks.
    • Regression runs on prompt or model changes.
    • Details of the current internal implementation to be confirmed by QualiteSoft before launch.

    Playwright · Node.js · Evaluation scripts · GitHub Actions

  • RAGPrototype

    Grounded Website Assistant

    The assistant on this website: answers only from approved QualiteSoft content, declines when unsure and offers to collect visitor details for the team.

    • Retrieval over services, process, industries, portfolio and FAQs.
    • Provider-agnostic (Claude / OpenAI) behind one interface.
    • Hard rules: never invent pricing, clients, results, certifications or guarantees.

    Next.js · TypeScript · Claude / OpenAI APIs

  • AI AutomationConcept

    Document Intake Automation

    Concept: extract, classify and validate contracts, invoices and forms into a system of record with confidence scores and human review queues.

    • Structured extraction with schema validation
    • Confidence thresholds route low-certainty items to people
    • Audit trail per decision

    LLM APIs · Zod schemas · Queue workers

  • AI SearchConcept

    AI Search Visibility Monitor

    Concept: track how AI answer engines describe and cite a business for target questions, and flag inaccuracies and opportunities for GEO work.

    • Question sets per client
    • Periodic answer sampling across engines
    • Citation and sentiment tracking

    Scheduled jobs · LLM APIs · Reporting dashboard

  • Future areasConcept

    Voice AI, Computer Vision & Document Intelligence

    Capability areas we are exploring as client demand grows: voice agents for booking lines, visual inspection for retail and hospitality, and document intelligence for professional services.

    • Evaluated per client need
    • Built on the same grounded, tested approach

    Speech APIs · Vision models · Document AI

Interactive

How a grounded assistant actually answers.

Step through a request the way the system sees it. The same rules run the assistant in the corner of this site: retrieve approved content, answer with sources, or decline and hand off.

1 / 4
assistant.tracesimulated
  1. POST /api/assistant session=anon provider=configured
  2. user: "Can you build a POS system for multiple restaurant locations?"
  3. guard length ok · rate-limit ok · pii-scan none

Simulated trace. Scores are illustrative; the retrieval rules, thresholds and refusal behaviour mirror the assistant running on this site.

AI / LLM testing

If it cannot be tested, it does not ship.

Our QA team tests AI features with the same rigour as payments or booking flows — with tooling built for probabilistic outputs.

  1. 01

    Golden datasets

    A curated set of inputs with expected outcomes, built with the client from real (anonymised) cases. Every change is measured against it.

  2. 02

    Evaluation rules

    Plain-language rules turned into checks: cite the source, never claim what the data does not show, use the agreed tone, decline when unsure.

  3. 03

    Regression on change

    A new prompt, a new model version or a new data source triggers the full suite. Differences are diffed and reviewed before release.

See QA & Test Automation, including AI/LLM testing
eval.career-intelligence.ymlillustrative
cites_member_answer
required
pass
no_psychological_claims
required
pass
two_profile_divergence
must differ
pass
hallucinated_fact
0 tolerated
pass
trigger
prompt | model | data change → rerun + diff
—

Modelled on the acceptance rules used for our AI career-coaching platform build: every insight must cite the member’s own answers, and two different profiles must produce visibly different results. Read the case study

Curious how AI could work for your business?

Let’s talk.

Bring a process, a dataset or a rough idea. We will tell you honestly whether AI is the right tool — and what it would take to build and test it.

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