OpenAI implementation that earns the right to reach production.
Ampity connects OpenAI models to real products and workflows through evaluation, data boundaries, guardrails, approvals, observability, and cost control.
OpenAI Select Partner
Ampity connects OpenAI models to real products and workflows through explicit evaluation, data boundaries, guardrails, human approvals, observability, and cost control.
Read the announcementA useful signal, with a clear boundary.
What it establishes
OpenAI describes its Partner Network as an ecosystem for partners that build, sell, deploy, and help customers connect to OpenAI technology.
Select is one of the program tiers. For Ampity, it supports work that identifies valuable workflows, integrates them with enterprise systems, and moves them into responsible operation.
Ampity remains accountable for workflow design, engineering, controls, evaluation, adoption, and operating evidence in each engagement.
What still has to be proved
Select Partner status does not mean every AI use case is viable or should use the same model.
It does not guarantee accuracy, safety, adoption, or return without representative evaluation and workflow change.
Consequential action should not be automated merely because a model can produce a plausible response.
The workflow comes before the demo
Ampity identifies the task, baseline, intended improvement, failure cost, and measure of value before selecting a model experience.
Evaluation shapes the design
Representative inputs, expected behavior, review criteria, latency, safety, and cost become a release test.
Access is deliberately constrained
Data, retrieval, instructions, tools, permissions, and human approvals are explicit boundaries.
The system can be operated
Traces, exceptions, feedback, model changes, cost, and quality drift are visible after launch.
The path from an impressive response to a trusted workflow
This is a buyer evidence model, not an AI architecture. It shows what should be proven before production use.
Choose a real workflow
Define the user, task, baseline, desired improvement, adoption condition, and cost of a wrong result.
Constrain context and action
Identify authoritative data, permitted tools, sensitive information, approvals, and abstention.
Test representative work
Measure quality, safety, latency, cost, and failure behavior against production-like examples.
Monitor it over time
Trace use, review exceptions, collect feedback, detect regressions, and control changes.
A production AI system is accepted because its behavior is measured and bounded, not because its best demonstration is convincing.
Where Ampity brings the platform into the work.
AI readiness and use-case validation
Rank workflows by value, feasibility, data readiness, failure cost, adoption friction, and evidence.
Use-case scorecard, risk register, success criteria, prototype decision
OpenAI product and API integration
Connect the selected capability to the product, identity, data, systems, and user journey.
Integration contracts, access boundaries, latency and cost baseline
Grounded assistants and RAG
Design retrieval, citation, freshness, permissions, and abstention so answers remain traceable.
Retrieval evaluation, source trace, permission tests, unsupported-answer handling
Agentic workflows
Model planning and tool use around typed actions, authorization, human checkpoints, idempotency, and recovery.
Tool contracts, approval matrix, audit trail, failure and retry tests
Evaluation and observability
Measure task quality, safety, latency, cost, and regressions with representative examples and traces.
Evaluation suite, release threshold, trace views, incident and rollback procedure
Adoption and operating controls
Integrate guidance, escalation, feedback, ownership, change review, and model lifecycle into the work.
Operating playbook, review cadence, feedback loop, model change record
One delivery path. Evidence at every stage.
Frame the workflow
Observe current work, define who benefits, identify failure modes, and agree how improvement is measured.
Workflow brief, baseline, and acceptance measures
Establish the trust boundary
Define data access, retrieval, tools, permissions, approvals, and insufficient-confidence behavior.
Data and action boundary with risk controls
Build and evaluate
Implement a bounded slice and compare quality, latency, safety, and cost against the baseline.
Evaluation report and controlled release candidate
Release and learn
Deploy with tracing, feedback, exception handling, ownership, and change control.
Operating workflow, monitoring, and review cadence
Begin with the decisions the system must survive.
How do you prevent a permanent pilot?
We define the owner, integration path, evaluation evidence, controls, adoption condition, and release decision before broad implementation.
When should a person remain in the loop?
Where mistakes carry material financial, legal, safety, customer, or irreversible operational consequences, or evidence is ambiguous.
How will we know a model change is safe?
A versioned evaluation set, production traces, regression thresholds, and controlled release path compare behavior before and after change.
Can Ampity work with our existing teams?
Yes. The model combines domain, product, data, security, and engineering knowledge rather than replacing the internal team.