01
Quality on your cases
Which errors would send a complaint to the wrong team or make a statement summary unusable? Count those errors against your current baseline.
Gyde Inference / scoped enterprise pilots
AI inference built around your workload and your rules
A model can score well on a benchmark and still fail your task, violate a data rule or cost too much once backup capacity is counted. Gyde tests one workload on agreed cases, serves it on approved infrastructure and records quality, failed requests and cost. You get evidence for a go/no-go decision.
Available for scoped enterprise pilots. Deployment geography and operating model are agreed per engagement.
GYDE / INFERENCEILLUSTRATIVE PILOT FLOW
DESIGN PATH / BUYER-SPECIFIC
01WorkloadTask + test cases
02ModelQualified candidate
03PolicyAllowed route
04ServingApproved capacity
05EvidenceOutcome + cost
Decision: proceed, redesign or stop
01Business problem
A good model score does not tell you whether you can use it.
A complaints classifier might work on public data but miss the categories your team uses. The same pilot must test the data path, failed requests and delivered cost.
02
An approved data path
The model cannot be used if requests, logs or fallback traffic reach a provider or region you have not approved.
03
Cost and interruption
A cheap GPU is not a cheap service if requests fail during replacement or standby capacity changes the bill.
02Gyde's approach
Test the task, approve the route and run the service.
One workload definition ties the model test to the serving policy and operating record.
Test the task
Compare a task-specific adapter, baseline and permitted fallback on the same frozen cases.
Read the detailApprove the route
Set the allowed location, provider, model, budget, fallback and purchase approvals before capacity is used.
Read the detailRun and inspect
Serve through the approved path. Record the route, request outcome, capacity event and available cost evidence.
Read the detail03Business value
The pilot should answer three buying questions.
These are deliverables for the go/no-go review, not promised savings or production availability.
| Buyer question | Evidence from the pilot |
|---|---|
| Is it good enough for this task? | Frozen comparison with your baseline, severe-error counts and the amount of human review needed. |
| Can we run it under our rules? | Approved data-flow and policy record, provider and region checks, and a failure drill. |
| What does an accepted result cost? | Successful, quality-acceptable request count with estimates and billed charges shown separately, including standby and failed starts where available. |
04Decision path
Follow the request and keep the record.
A human approves pilot scope and purchase policy before capacity is used.
01Workload and test cases→︎
02Candidate models→︎
03Policy checks→︎
04Approved capacity→︎
05Request routing→︎
06Outcome and cost evidence
05Pilot candidates
Start where errors and review ownership are clear.
01
Complaint operations
Suggest categories or routing for human review, using the institution's own taxonomy.
Explore this area02
Statement analysis
Explore transaction labels and deterministic summaries under reviewer oversight.
Explore this area03
Other structured tasks
A classification or extraction task can work if your team can supply cases, a baseline and an owner for exceptions.
Explore this area06Current status
What is proven, and what needs a pilot.
01
Demonstrated in controlled tests
Model and operational tests exist under documented conditions. See their limits alongside the results.
Explore this area02
Deployed, still in live acceptance
Supplier integrations and the control plane have been exercised in one geography. Some controls are deployed but are not yet proven under live load.
Explore this area03
Customer deployment design
Customer-cloud and customer-site arrangements need design, packaging and acceptance in the buyer's environment.
Explore this area07Next step
Bring one workload and your current baseline.
Name the errors you cannot accept, where the data may go and who owns the result. Gyde and your team can then agree on the test, deployment conditions and pass/fail criteria before traffic starts.
Design a pilot?Buyer questions
Questions to settle before a pilot.
Answers reflect the current prototype and scoped engagement approach.
What is Gyde Inference?
Gyde Inference is a scoped pilot to test whether a model can do one task under your quality, data location, budget and operating rules. Gyde compares models, serves through an approved route and records the result so your team can decide whether to continue.
Can it run in our cloud?
A customer-cloud deployment can be assessed as part of a pilot. The current prototype is not a packaged customer-cloud install.
Does the system make lending decisions?
No. Models can support routing, extraction and triage with human review and lender-owned decision rules.
Gyde Inference pilot
Start with a workload you can measure.
Describe the task, data boundary and decision you need to make. We will scope the test and operating conditions together.
Discuss a workload Do not submit sensitive records through the marketing form.