AI and automation
OpenAI
Technology in service of the outcome.
Build useful AI features and business workflows with OpenAI models where task quality, data requirements and operating economics support the choice.
- Prompt
- Named model
- Evaluation
- Approved output
- Blocked
- Trace log
Evaluation gate showing a prompt entering a named OpenAI model, an evaluation step, then allow or block.
An enterprise assistant becomes useful when it can answer a real question, identify the information behind its answer and move an authorised task forward. Emerge designs OpenAI applications around those operating requirements. We begin with the decisions people make, the systems they use and the consequences of an incorrect response, then choose the model, retrieval pattern and user experience that fit.
We build knowledge assistants, structured information extraction and agents connected to business tools. OpenAI is one model option within a technology-independent architecture. The application retains ownership of identity, permissions, task state and execution records, so changing a model does not require rebuilding the business process. Our public AI perspectives and platform pages explain this approach; they are not claims of a formal OpenAI partnership.
Choose the right starting point
Product expertise, in detail.
Enterprise assistants
Source-backed answers, document preparation and escalation, with an evaluation set tied to the questions your teams actually ask.
Tool-connected agents
Bounded actions through business APIs, using typed inputs, approval rules and observable execution rather than unrestricted account access.
Evaluation and operations
Task scoring, model comparison, release gates, usage allocation and recovery paths for AI services operating beyond the pilot.
The starting point
What needs to change.
A general chat interface cannot determine which contract, customer record or operating procedure a particular employee is allowed to read. Retrieval must inherit the user’s access and distinguish current approved material from superseded drafts.
A model can propose a plausible action without completing it. Customer updates, bookings and document changes need explicit tool contracts, validation, authorisation and a receipt from the destination system.
An impressive demonstration can conceal weak handling of missing information, ambiguous requests or unavailable integrations. Release decisions need representative task evidence and a defined operating cost per successful outcome.
What we deliver
From opportunity to working systems.
An answer with an accountable source
We map a knowledge collection to named owners, preserve document and section identifiers, and test retrieval against the same access rules used by the underlying system. The interface exposes relevant sources and makes insufficient evidence a normal, useful outcome.
A controlled next step
We separate reading information from changing a business record. The agent prepares an action with validated fields; the application checks the user’s authority and any review requirement before execution. A confirmed result is reconciled with the requested change.
An operating feedback loop
We capture task outcome, latency, tool failure and metered usage without collecting unnecessary sensitive prompt content. Failed examples return to a reviewed evaluation dataset and a prioritised improvement backlog.
Illustrative solution architecture
How the pieces work together.
Bring context into the workflow, connect the right solutions, and make progress visible.
01 Understand the context
Signals & knowledge
Use the context and information already in place.
- Business priorities
- Trusted knowledge
- Operational data
02 Connect the solutions
An answer with an accountable source
A controlled next step
An operating feedback loop
03 Put it to work
Teams & operations
Connect people and systems to the next useful action.
Measured outcomes
Track agreed measures, learn, and improve the workflow.
Built around your existing technology
Implementation design
How the pieces work together.
Responses API and application state
The Responses API supports model interactions and tools. Emerge places it behind an application service that controls credentials, conversation retention and allowed capabilities. Business state remains in the appropriate CRM, workflow engine or application database; model output is an input to that process.
Retrieval before generation
Approved documents pass through classification, text extraction, indexing and permission-aware retrieval. The response includes source context. Where transactional accuracy matters, such as an order status or account balance, a scoped live query takes precedence over a stale document fragment.
Decisions to make early
Model fit and economics
Model selection considers task quality, response time, context requirements and total cost of a completed workflow, including retrieval and retries. We do not assume the largest model is the best default for routing, extraction or every conversational step.
Product and access boundaries
ChatGPT subscriptions, API projects and deployment through a cloud channel have different administration and commercial arrangements. We confirm the actual product, supported tools, data controls and account entitlements during design rather than treating them as interchangeable.
Our approach
A clear path into delivery.
Start with the business problem. Make each stage useful, reviewable and owned.
- 01
Discovery produces a task map, information boundary, tool inventory and acceptance rubric. We select a small set of representative high-value tasks and include refusal, clarification and escalation cases from the start.
- 02
A working increment connects one approved knowledge source or tool, with test identities, structured output validation and replayable examples. The business reviewer checks usefulness while engineering verifies permissions and destination receipts.
- 03
Release includes evaluated model configuration, deployment instructions, cost allocation, incident ownership and a rollback path. Any later model or prompt change is checked against the retained task suite before wider use.
Go deeper
Build a more informed brief.
Related Agentforce integration guides for OpenAI.
These integration guides examine specific systems, access boundaries and illustrative use cases.
Practical questions
Before we begin.
Can the assistant update our CRM?
Yes, where the CRM exposes a suitable interface and the organisation approves the action. We scope permissions to the required objects, validate the proposed change, protect against duplicate requests and return a clear result or recovery path.
How do you measure whether it is ready?
We agree what a successful task looks like before implementation. Testing covers source correctness, permission boundaries, required fields, appropriate escalation and completed tool actions. Reviewers examine representative failures alongside aggregate scores; a fluent answer alone does not count as success.
Explore the detail
Related work and resources.
See the approach in context. Client engagement stories are anonymised; related examples may come from other sectors or platforms.
Connected expertise
Technologies
Platforms
Your next move
Bring us the business problem.
Pick a time below. We will help you define a useful starting point, the expertise you need and a practical path to delivery.
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