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XYPHORS
A — AI & AutomationService 02 · B2

Intelligence youcan check.

Retrieval over your own material, extraction from inputs that were never structured, assistants scoped to one real workflow. Every deployment ships with an evaluation set, so quality is a number you can watch instead of an impression you defend.

Describe a taskAll services
X
EvidenceRetrieval, sources, evaluation, thresholds.
Y
ResponsibilityWhat the model may decide, and what it must escalate.
B — Where it pays off4 situations

The question is never whether a model can. It is whether the task can be checked.

These four situations are worth automating. Anything that cannot be scored against real cases is worth prototyping and then leaving alone until it can be.

  1. 01

    A high-volume task with a human bottleneck

    Reading, classifying, extracting, replying. Worth automating precisely because the inputs are messy — but only with a defined escalation path for the cases that should not be automated.

  2. 02

    Answers with nothing behind them

    An assistant that cannot show where an answer came from cannot be checked, and will not be trusted twice. Retrieval with citations is the baseline, not a feature.

  3. 03

    The demo that never became a feature

    A prototype that impressed everyone and shipped to nobody, because the workflow around it — approvals, exceptions, audit — was never designed.

  4. 04

    No way to tell whether it got worse

    A new prompt, a new model version, a changed document set. Without a scored test set, every one of those is a blind change.

C — The loopInputs → evaluation
OrchestrationC — figure 01
INPUTSRETRIEVEREASONOUTCOMEDocumentsRecordsRequestsCorpus+ CITATIONSModelSCOPED TOOLSAutomatedEscalatedEVAL GATESCORED CASES RETURN HERE
System stepModel step and gateOne request, traced
Retrieval grounds the input, the model works inside a scoped set of tools, and the result either acts or goes to a person. The dashed return path is the part usually missing: scored cases coming back to change the next version.

Five stages, and only one of them is the model

Most of the engineering in an AI feature is not the prompt. It is the retrieval, the routing, the failure behaviour and the evidence trail — the parts that decide whether anyone can trust the output next quarter.

  1. 01

    Inputs

    documents, records, requests

  2. 02

    Orchestration

    retrieve, route, call

  3. 03

    Reasoning

    the model's step

  4. 04

    Automation

    the action taken

  5. 05

    Evaluation

    scored against a test set

D — What we build4 shapes of engagement
D1

Retrieval over your own material

Answers grounded in your documents and records, with the source attached and the boundary of the corpus made explicit.

  • Ingestion and chunking
  • Hybrid search
  • Citations
  • Access control
D2

Extraction from unstructured input

Turning documents, emails and forms into typed records, with a confidence signal and a review queue for the cases that need one.

  • Schema-constrained output
  • Confidence thresholds
  • Review queue
  • Corrections loop
D3

Assistants scoped to a workflow

Not a general chatbot. One job, inside the system where the work already happens, with the tools it is allowed to call written down.

  • Tool use
  • Guardrails
  • Session context
  • Handover to a human
D4

Evaluation harnesses

A test set built with the people who do the work, run on every change, reported as a number with the failures attached.

  • Labelled cases
  • Regression runs
  • Failure analysis
  • Release gates
E — EvaluationEvery change, scored

A number you can watch, not an impression you defend.

The test set is built with the people who do the work today, and it is the first deliverable — before any feature ships. After that, every change has a before and an after.

We also report the failure modes we know about. A model that is weak on a specific document type is a fact about the system, not an embarrassment.

Evaluation gate4 changes
ChangeWhat we measureWhat has to hold
A new promptScored test setNo regression on labelled cases
A new model versionSame test set, re-runCost and latency inside budget
A changed corpusRetrieval hit rateCitations resolve to a real source
A new taskCases labelled with the teamEscalation path agreed first
F — CapabilitiesStack, by layer
L1Models
LLM applicationsStructured outputEmbeddingsPrompt design
L2Retrieval
Vector and keyword searchChunking strategyRe-rankingCitations
L3Orchestration
Tool callingQueuesRetriesCost controls
L4Assurance
Evaluation setsHuman reviewAudit trailMonitoring
  1. 01

    Name the task precisely

    Not a directive to use AI in support — one task, with inputs, an output, and a definition of a correct answer.

  2. 02

    Build the test set first

    Real cases, labelled by the team that handles them today. This is the contract the system is measured against.

  3. 03

    Build the loop, not the demo

    Retrieval, the model call, the action and the review path, designed together — the workflow is where this is won or lost.

  4. 04

    Report where it is weak

    Every deployment arrives with the failure modes we know about, and the threshold at which a human still decides.

G — Contact06 Clarity · resolved

Bring one task, not a strategy.

Describe the work someone does by hand every day. We will tell you whether a model helps, what it would take to evaluate it honestly, and where we would leave the human in the loop.

Describe the taskRead our notes