01
Retrieval-augmented answers
Questions are answered from your own documents and records via Azure OpenAI Service, with citations and a confidence signal.
Outcome: useful answers, traceable to a source.
Azure OpenAI Service integration specialists
Azure OpenAI Service API integration and platform bridging
Getting Azure OpenAI Service to talk to the rest of your systems means keeping prompt versioning, token usage and model routing correct on both sides, automatically. That is the integration work we do.
Fixed-scope engagements, delivered by UK-based integration engineers. Documentation and source code handed over on completion.
- ✓Azure-hosted OpenAI models
- ✓Private networking (VNet)
- ✓Content filters & quotas
What we can connect Azure OpenAI Service to
Azure OpenAI Service is an API-first AI platform. We put it to work against the data and processes you already have — your CRM, help desk, documents and back-office systems — through the Azure OpenAI Service API, with retrieval so answers are grounded in your own records, and a gateway that logs every call and caps spend.
CRM and sales
Summarise account history, draft replies and score or route leads inside the CRM with Azure OpenAI Service, every request logged against the record.
Salesforce, HubSpot, Dynamics 365, Pipedrive
Help desk and support
Draft responses, surface the right knowledge-base article and triage tickets with Azure OpenAI Service, with a person approving the send.
Zendesk, Freshdesk, Intercom, Gorgias
Documents and knowledge base
Index your documents into a vector store and answer questions with citations using Azure OpenAI Service — retrieval-augmented, not from the model's memory.
SharePoint, Confluence, Google Drive, S3
Ecommerce and catalogue
Generate product copy, categorise SKUs and answer customer questions against live catalogue and stock data with Azure OpenAI Service.
Shopify, WooCommerce, Magento, BigCommerce
Data warehouse and BI
Natural-language querying, classification and enrichment of warehoused data with Azure OpenAI Service, results written back as columns.
BigQuery, Snowflake, Postgres, Power BI
Bespoke and back-office systems
Add extraction, drafting and classification to an in-house application through a documented service that wraps Azure OpenAI Service and enforces your rules.
SQL Server, internal APIs, SFTP, email
Common Azure OpenAI Service integration use cases
Each is a defined, testable data flow rather than an open-ended development project.
02
Inbox and ticket drafting
Azure OpenAI Service drafts a reply from the conversation and your knowledge base; an agent edits and sends.
Outcome: faster first responses, consistent tone.
03
Document extraction
Invoices, contracts and forms are parsed into structured fields by Azure OpenAI Service and pushed into the ERP or CRM.
Outcome: less manual keying, fewer transcription errors.
04
Classification and routing
Incoming messages, leads and cases are categorised and routed by Azure OpenAI Service against your own taxonomy.
Outcome: the right team sees it first time.
05
Content generation at scale
Descriptions, summaries and first drafts generated by Azure OpenAI Service from structured source data, reviewed before publish.
Outcome: coverage without a copywriting bottleneck.
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Guardrails, logging and cost control
Every Azure OpenAI Service call runs through a gateway that fixes prompts, redacts PII, caps spend and records the full request and response.
Outcome: AI you can audit and budget for.
How a Azure OpenAI Service API integration is delivered
Four stages, fixed scope agreed before any code is written. Typical delivery for a single-system integration is two to four weeks.
Stage one
Discovery and data mapping
We document the systems involved, the direction of each flow, and every field that must move — including the awkward ones such as tax, discounts and multi-currency.
Output: written integration specification.
Stage two
Fixed scope and estimate
The specification becomes a fixed-price proposal with a delivery schedule and defined acceptance criteria. No time-and-materials open ends.
Output: costed scope for sign-off.
Stage three
Build and testing
Development against a Azure OpenAI Service development or sandbox environment and a sandbox of the destination system, with your own data used for acceptance testing.
Output: tested integration and test evidence.
Stage four
Go-live and handover
Controlled cutover with monitoring in place, followed by handover of code, credentials and documentation, and a period of hypercare support.
Output: live integration, owned by you.
Have a Azure OpenAI Service data flow in mind? We will tell you what it takes before you commit.
Request a scopeAzure OpenAI Service API bridging when there is no direct connector
The hard part is rarely Azure OpenAI Service itself. It is making sure the system on the other end of the wire — often older, undocumented, or maintained by someone who has since left — still gets what it needs, in the shape it expects.
That layer normalises data formats, holds a queue so neither platform is overwhelmed, authenticated the way Azure OpenAI Service expects — Microsoft Entra ID / API key — and records every transaction so a disputed record can be traced end to end. It also isolates you from change: when Azure OpenAI Service deprecates an API version, only the bridge is updated.
If there is no usable interface at all, the fallback is a flat-file exchange, a direct database connection, or a small REST wrapper we write and host ourselves — whichever fits what is actually there.
Included in every bridge we build
- →Error handling with alerting when a transfer fails
- →Automatic retries that respect the platform API rate limits
- →A transaction log for tracing any individual record
- →Idempotency so a repeated event cannot create a duplicate
- →Field-level mapping documentation you can hand to any developer
- →A staging environment that mirrors production
Azure OpenAI Service integration FAQs
Not on the business API tiers we build against — paid API terms exclude training on your prompts and outputs, and we enable zero- or short-retention options where the provider offers them. The exact terms for your account are confirmed in writing during discovery.
Retrieval-augmented generation: Azure OpenAI Service is only asked to answer from documents and records supplied at query time, responses carry citations, and low-confidence or unsupported answers are held for a human. The model is never the source of truth.
Every call goes through a gateway that caps tokens per request, rate-limits per user or workflow, caches repeated queries and logs cost per feature. You get a monthly figure per use case and an alert before a budget is reached.
You do. Source code, credentials, configuration and documentation are handed over on completion, and there is no licence fee for continuing to use it. Monitoring and change support are available under a separate agreement.
Other platforms we integrate
Each platform has its own integration guide covering authentication, available endpoints and typical data flows.
