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BayetoThornbury Fixed Income (demo)
Demo — fictional firm,
real engine output
Analyze your own system
All recommendations
Outstandingobjective: lowest costCostlier region than neededcomplexity Medium

Pin Portfolio Analytics serving to the caller’s region

Route Portfolio Analytics requests to an in-region deployment (EU callers → EU serving)

Verify before implementing — this recommendation rests on evidence the engine does not have:

  • No compliance/data-residency constraints on record for this app.
  • No independent quality evaluation — the quality impact is unmeasured.

Implement only if these checks pass. The full self-critique is in the reasoning trail below.

Annual value€1,053bill €21 + latency €1,031
Payback periodnot economic≈€820 to implement · Medium effort
Confidence90%evidence High
Sample size17,280requests observed

Payback divides by an implementation cost of €820 under the agent-assisted-v1-2026-08 cost model: 8h of human review at €100/h plus a €20 agent-run allowance — an AI agent implements, your team reviews and verifies. Implemented by hand instead, the same move is estimated at 6 engineer-days (≈€4,800). Every figure is a prior, not a measurement of your team — and the model is versioned so a changed assumption can never move a figure silently.

Context completeness

Confidence 90% is how sure the engine is on the evidence it has. This is how much of the evidence that could change the conclusion the engine could establish for this application — a different question. The declare-context page counts what you have answered workspace-wide, which can legitimately read differently: an “I don't know” is an answer there and establishes nothing here.

4/6
dimensions present
  • Telemetry
  • Provider bill— calibrated from the monthly spend declared for this workspace, not a reconciled invoice
  • Per-app criticality
  • Prior experiments
  • Codebase
  • Quality evaluation

We cannot detect Codebase, Quality evaluation — you can tell us, and confidence is re-derived on the fuller evidence.

Observation → Evidence → Inference → Recommendation

every fact, judgment, self-critique and failure condition behind this decision
1

Observations (facts)

  • [cross_region_share] 42.7% of Portfolio Analytics requests are served outside the caller's region. (N=17,280)
  • [p95_latency_ms] Portfolio Analytics p95 latency is 1743ms (p50 1244ms). (N=17,280)
2

Evidence (objective-relevant)

  • 42.7% of Portfolio Analytics requests are served outside the caller's region; p95 latency is 1743ms. (strength 0.7137)
3

Inference (judgment)

Cross-region serving is adding avoidable latency to a latency-sensitive workload; in-region serving is the Pareto-dominant move under a latency objective.

4

Recommendation (move)

Route Portfolio Analytics requests to an in-region deployment (EU callers → EU serving)

Portfolio Analytics: cross-region serving on the hot pathPortfolio Analytics: Route Portfolio Analytics requests to an in-region deployment (EU callers → EU serving)
5

Critical Review (self-critique)

Assumptions

  • Assumes cross-region serving is an accident of routing, not a deliberate choice.

Alternative explanations

  • The cross-region hop may be REQUIRED by data-residency / compliance policy; pinning to the caller's region could violate it.

Missing evidence

  • No compliance/data-residency constraints on record for this app.
  • No independent quality evaluation — the quality impact is unmeasured.

This is wrong if…

  • Wrong if regulation mandates where this workload's data is processed.

Download the canonical decision record (JSON) — the deterministic decision block (byte-pinned by CI), the run context (engine + catalog versions, measured-window boundaries, exact pricing rows, fixed FX with effective date, formula inputs, the overlap group, and the ledger version), and the LLM-phrased narrative as a visibly separate section.

Business case & technical detail

the phrased explanation — the decision above is computed without it

Business case

Portfolio Analytics is latency-sensitive, yet 42.7% of its traffic takes a cross-region round trip; in-region serving removes that hop. Estimated €21/yr at the current run-rate, payback not economic, confidence 90%.

Technical detail

Deploy the Portfolio Analytics inference path in the caller's region and route by user geography; keep US as failover. Affects ~9,232 req/mo; expected cost -3%, latency -10.7%, quality unchanged by construction.

Benchmark context & Optimization Memory

your value against the cohort, and what prior outcomes taught the engine

Benchmark context

Share of traffic served outside the caller region (lower is better). (illustrative)

your 43%median 12%top-quartile 3%

Optimization Memory

Learned from 3 prior implementations, 3 succeeded (N=62,000).

Why NOT the alternatives?

each rejected route, with the constraint that rejected it
  • Add an in-region read replica cache
    cost+4%latency-10%quality+0%Helps, but only for cacheable reads; in-region serving fixes the whole path and is cheaper to run.
  • Route to a US-only cheaper provider
    cost-15%latency+5%quality+0%Cheaper but keeps EU traffic on US infra — worsens latency and breaches EU data-residency (privacy).

Implementation packet

advisory_onlyrisk: routing

Reviewed by Bayeto engineering on 2026-07-26 · rule version v1 · 94% of the €56,350/yr open on this workspace is carried by a packet an engineer can execute (5 of 8 open moves); 3 of 14 specs meet that standard. Missing mandatory for this risk class: guardrails, stopIf, rollback.

Review attests that these steps are sound and reversible as written. It does not guarantee an outcome: whether the change saves money is measured by the validation loop afterwards, never promised by the reviewer.

Not reviewed to executable standard

Guardrails — watch these, Stop if, Rollback are all unwritten for this move, for one reason: Not reviewed to executable standard. Region is frequently a DATA-RESIDENCY constraint rather than a performance choice, and Bayeto cannot see the contractual or regulatory reason a workload is pinned where it is.

What you would need to decide

Bayeto withholds steps here because these are your calls to make, not ours. Answer them and this move can be specified.

  • Is this workload pinned to its region by a contract, a regulation, or a default nobody revisited?

    Bayeto sees where calls landed, never why. A residency obligation and an unexamined default look identical in telemetry, and only one of them is movable.

  • Does any data in these requests carry a residency or transfer restriction?

    Bayeto observes metadata about calls, not their contents or their classification, so it cannot know what may lawfully leave a jurisdiction.

  • Preconditions1 specified instruction (mandatory for this risk class)
  • Steps1 specified instruction (mandatory for this risk class)
  • Guardrails — watch thesenone specified (mandatory for this risk class)withheld — for the reason stated above
  • Stop ifnone specified (mandatory for this risk class)withheld — for the reason stated above
  • Quality validationnone specifiedwithheld — Not reviewed to executable standard. Region is frequently a DATA-RESIDENCY constraint rather than a performance choice, and Bayeto cannot see the contractual or regulatory reason a workload is pinned where it is.
  • Rollbacknone specified (mandatory for this risk class)withheld — for the reason stated above

Instruction text is withheld here: the public demo shows complete packets only for the moves reviewed to the executable standard — 3 of 14 specs — as its worked examples; the rest summarize what exists and what was deliberately not specified.

Simulate — Before You Build

Projected annual savings€21scenario realization (sim-v1 assumption) at 90% confidence
Downside / base / upside€15 · €21 · €21realization = 0.7 + 0.3 × 90% = 0.97 · base €21 · sim-v1
cost-3%latency (prior)-10.7%quality+0%

The cost delta derives from this workspace’s telemetry. The latency and quality deltas are heuristic priors carried by the rule: no quality evaluation is connected, and no rule measures the latency effect of its own move — the figures are per-rule constants, so nothing here observes either outcome.

  • · Applies to ~9,232 requests/month at current mix.
  • · Scenario realization assumption (sim-v1, not a statistical expected value): 0.7 + 0.3 × confidence = 0.97. The 0.7 floor encodes implementation shortfall — even a certain move rarely captures 100% at rollout; rationale published at bayeto.ai/methodology.
  • · Per-request cost/latency/quality deltas hold at current traffic shape.
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