Service
Enterprise AI Integration Services
XphereInfo provides enterprise AI integration services that connect large language models to the systems you already run: ERPs, CRMs, legacy platforms and internal data.
We design LLM orchestration, secure RESTful APIs, vector databases and data transformation pipelines, then deploy and support them with the same team.
The goal is practical: assistants that answer from your own documents, workflows that draft and route work, and agents that act on your systems only within the permissions you set. We start with one high-value workflow, prove it against real examples, and expand from there.
What enterprise AI integration includes
Enterprise AI integration is the engineering work that turns a capable model into a dependable part of your business. The model is the easy part. The hard part is giving it the right context, the right permissions and a safe way to act on your systems.
A typical engagement covers:
- Use-case selection and a written success measure for each workflow
- Data access: connectors, permissions and data transformation pipelines
- Retrieval and orchestration logic that grounds answers in your sources
- APIs, SDKs and user interfaces your teams actually adopt
- Evaluation, monitoring and cost controls once it is live
Our approach: LLM orchestration, RAG and vector databases
We start with retrieval-augmented generation (RAG) for most knowledge workloads. Your documents and records are chunked, embedded and stored in a vector database, then retrieved at query time so the model answers from current, permissioned data rather than memory.
LLM orchestration sits on top. It routes requests, calls tools, enforces schemas on outputs and falls back gracefully when a model is slow or wrong. We pick models per task, keep prompts versioned and test every change against an evaluation set before it ships.
Fine-tuning is used only when retrieval cannot reach the quality or format you need.
Connecting AI to legacy systems and data transformation pipelines
Most valuable enterprise data lives in systems that were never designed for AI: mainframe exports, on-premise ERPs, decades-old databases and shared drives. We do not ask you to replace them first.
Instead we wrap them in secure service layers and build pipelines that extract, clean, normalize and enrich data on a schedule or in real time. Change-data capture keeps the AI layer current, and every transformation is logged so you can trace an answer back to the source record.
APIs and custom SDKs for your teams
AI capabilities only matter if your developers can use them. We expose each capability through documented RESTful APIs with versioning, authentication and rate limits, so product teams can build on them like any other internal service.
Where it speeds adoption, we ship typed SDKs for the languages your teams use, along with examples, sandbox environments and OpenAPI specifications. The same interfaces also let models call your systems as tools, with every action scoped by role.
Security, governance and compliance
Enterprise data stays under your control. We design every integration around least-privilege access and keep a full record of what the AI saw and did.
- Single sign-on and role-based access, enforced at retrieval time
- Audit trails for prompts, retrieved sources, outputs and tool calls
- PII detection and redaction before data reaches a model
- Private networking and region pinning when your policies require it
- Documented data-retention settings with every model provider
How we work
Engagement model: Discover, Build, Deploy, Support
One team owns the integration from the first workshop to ongoing support, so nothing is lost in handoffs.
- Step 1
Discover
Map systems, data and risks. Pick one workflow and agree a measurable goal.
- Step 2
Build
Ship a working slice in short iterations, evaluated against real examples.
- Step 3
Deploy
Roll out behind access controls with monitoring, cost limits and rollback plans.
- Step 4
Support
The same engineers tune quality, update models and extend to new workflows.
Proof
Case studies
Placeholder
Case studies coming soon
We are preparing approved, client-reviewed write-ups of recent AI integration work. Ask us for references on a scoping call.
FAQ
Questions about AI integration
What is enterprise AI integration?
It is the work of connecting AI models, usually large language models, to the systems, data and permissions an organization already has, so the AI can answer questions and complete tasks safely inside real business workflows.
How do you connect an LLM to legacy systems?
We wrap legacy systems in secure, documented APIs and build data pipelines that move and clean their data into a retrieval layer. The model then reads through retrieval and acts through scoped API tools, so the legacy system itself does not need to change.
How long does an AI integration take?
It depends on data readiness and scope. We agree a timeline after a short discovery phase and deliver a first working workflow in small, reviewable increments rather than one large launch.
How do you keep enterprise data secure?
We apply least-privilege access, enforce your existing permissions at retrieval time, redact sensitive data before it reaches a model, keep audit logs of every interaction and configure providers so your data is not used for training.
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