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AI Hiring Intelligence Platform

Hiring, run as a governed system.

Requirements enter once, compile into a versioned rubric, and every step after that is recorded.

Record per role
1Record per role
Scores rubric-versioned
100%Scores rubric-versioned
Audit trail
Append-onlyAudit trail
An operations lead reviewing hiring performance on a wall display in a modern office
  • One record per role and candidate
  • Rubric version on every score
  • Approval gate before release
  • Append-only audit trail

System architecture

Pick a module. See exactly what it does.

What enters it, what it does, what it produces, what you control, and what gets recorded.

Role requirements

Hiring outcomes

Outcomes feed the Talent Graph, which the next role's rubric reads from.

Intake Engine

In the product

Runs in: Intake and role launch

What enters it
  • Role brief, must-haves, nice-to-haves, constraints
  • Job description upload (PDF)
  • Compensation range, location and work-permission rules
What it does
  • Validates every field against a schema before it is stored
  • Saves drafts so a partial intake is never lost
  • De-duplicates repeat submissions by idempotency key
What it produces
  • A validated requisition record tied to your organisation
What you control
  • Edit any field before the role is launched
  • Role intensity: steady, standard or aggressive
What gets recorded
  • Who submitted the intake, when, and every later edit

Evidence graph

Every score traces back to a quote.

Requirement, evidence, source, rule, points, decision. Pick a requirement and follow the whole chain. Where evidence is missing, it says so.

Evidence graph — a scored candidate

Representative data for one senior platform role. In the workspace this is the real record, with reviewer history attached.

Representative data
5 requirements3 with verified quotes1 quoted but unsettled1 with no evidence1 conflicting1 reviewer-confirmed
  1. 1

    Requirement

    5+ years operating Kubernetes in production

    Must-have · sits in the must-have block, worth 60% of the score in total. Matched terms: kubernetes, production.

  2. 2

    Candidate evidence

    Confirmed by reviewer
    status: met
    stored confidence 92%

    A reviewer read the source and accepted or corrected the evidence.

  3. 3

    Source and quote

    • cvCV · characters 1180–1264
      Ran the production Kubernetes platform (42 services, 3 regions) for six years as staff engineer.
    System interpretation

    Six years of platform ownership at production scale, stated with system counts.

  4. 4

    Scoring rule

    Status "met" credits 1 of this requirement inside the must-have block (60% of the score).

  5. 5

    Score contribution

    20 of 20 ptscredit 1 inside the 60% block
  6. 6

    Decision impact

    Supports approval — this must-have is evidenced and shown to the client as a strength.

Weights and credits shown here restate the rules the scoring engine already applied. This view reads records; it never recalculates or edits a score.

Decision Workspace

The same screens your team works in.

A role moves through nine stages, and each one names its owner, its outputs and what is blocking it. Decisions sit next to the evidence.

Role lifecycle · Senior Platform Engineer

Review · waiting on your team since 3 Aug

Representative data

Workflow

Review · waiting on your team since 3 Aug

3 need attention
Representative data — a worked example of the product, not a live account. No candidate information is shown.
Decision Workspace · Senior Platform Engineer

3 decisions due · Rubric v4 · locked

Representative data
  • Candidate ref 4F2K9Q🦄

    7 of 8 requirements evidenced · Awaiting your decision

    98
    Top fit
    • Ran a 42-service Kubernetes platform across 3 regions for six years.
    • Escalation owner on a customer-facing on-call rotation.
    • Gap: No SOC 2 audit experience found in the CV.
    Reversible for 5 minutes
  • Candidate ref 8HD3TW

    6 of 8 requirements evidenced · Awaiting your decision

    84
    Strong fit
    • Migrated a monolith to managed Kubernetes over two quarters.
    • Wrote the infrastructure-as-code standard adopted by four teams.
    • Gap: Team-leadership evidence is partial — one quote, no scope.
    Reversible for 5 minutes
  • Candidate ref 2QL7BM

    5 of 8 requirements evidenced · In review by TaaSFlow

    76
    Consider
    • Strong reliability work, mostly on internal-only services.
    • Gap: Production scale is below the blueprint threshold.
    Reversible for 5 minutes
Representative data — a worked example of the product, not a live account. No candidate information is shown.

Governance

The controls that sit over the system.

People stay in the loop as governance — approving, escalating and overriding, with each action logged.

Approval gates
No candidate reaches your shortlist until evidence is verified and the release is approved.
Scoring rubric versions
Each score names the rubric version it ran on. Versions are frozen once scored against.
Role-specific configuration
Weights, must-haves and intensity — steady, standard or aggressive — are set per role.
Agent status
Enable, pause or disable any agent per role. Paused agents stop; the change is logged.
Audit history
Every state change, override, release and access event is appended, never edited.
Human escalation
Escalation path to a named platform expert inside the workspace.
Two colleagues reviewing candidate profiles on a large screen

Decision Workspace

Every shortlist arrives with the evidence that produced it.

Candidate comparison
Side by sideCandidate comparison
Every score
Rubric-versionedEvery score
Every decision
RecordedEvery decision

Talent Graph

Each closed role makes the next one shorter.

Not a model that trains itself. Verified outcomes, retained evidence and role memory that your next rubric can read.

  1. 01

    Outcome is recorded

    Hire, reject or withdrawal — with the reason captured at decision time.

  2. 02

    Evidence is retained

    The verified evidence behind that outcome stays attached to the record.

  3. 03

    Role memory is written

    What worked and what to avoid is kept as notes against the role family.

  4. 04

    Next role reads it

    New rubrics resurface past candidates and inherit role memory, so briefing starts ahead.

Retained data stays inside your organisation. You can edit or remove any record, and every re-engagement is logged.

Where the neighbours stop

Adjacent categories, and the gap they leave.

CategoryWhat it doesWhere it stops
Applicant Tracking (ATS)Stores records.Doesn't source, doesn't score, doesn't produce a shortlist.
Recruiting agencyDelivers candidates.Doesn't leave a system behind — the pipeline goes with the vendor.
AI sourcing toolFinds profiles at scale.Doesn't run intake, doesn't score against a rubric, doesn't hand off to hiring.
Talent CRMKeeps warm leads.Doesn't produce ranked, evidenced shortlists on active roles.
TaaSFlowRuns the whole loop — intake, rubric, agents, evidence, scoring, decisions and memory — in one system you keep.
  • Not only an ATS
  • Not only an agency
  • Not only AI sourcing
  • Not only a CRM

Ready to see it

Open a role. Watch the system run.

Submit intake and see the compiled rubric, agent runs, and first evidence-backed shortlist inside your workspace.