:: AI services / Enterprise AI development
Enterprise AI development and AI integration services
AI only pays off once it is connected to the systems your people already use. We integrate AI into Salesforce, ERP, document stores and internal tools, with the security, permissions and audit trails a larger company needs.
Salesforce, ERP and internal systems
SSO and role-based access
Audit trail on every action
:: What we do
AI integration services
We work with what you have. Most of our enterprise AI work is integration, not new systems.
01
Salesforce AI integration
AI inside Sales, Service and Experience Cloud: lead scoring, case summaries, reply drafts and Agentforce setup, using your existing data model.
02
ERP and finance integration
Connect AI to SAP, NetSuite, Dynamics or Odoo for document intake, reconciliation and forecasting.
03
ChatGPT and LLM integration
Add OpenAI, Claude or Gemini to your internal tools through secure enterprise APIs, with usage limits and logging.
04
Knowledge and document systems
Make SharePoint, Confluence, Google Drive and file servers searchable by AI, respecting existing permissions.
05
Data platform work
Pipelines from your warehouse or lake to AI services, with data quality checks and lineage.
06
Governance and security
Single sign-on, role-based access, data masking, audit logs and policies that satisfy your security team.
:: Integration patterns
Where AI connects to the systems you already run
Most enterprise AI is not a new app. It is AI working inside Salesforce, your ERP, your document stores and the tools your people already open every day. These are the patterns we build most.
CRM
AI inside Salesforce
Agentforce or custom agents that triage cases, summarise accounts and draft replies, respecting Salesforce permissions.
Read the blueprint
ERP
Document AI into your ERP
Invoices, orders and delivery notes read by AI and posted as clean records, with exceptions queued for people.
Read the blueprint
Knowledge
Assistant on SharePoint, Confluence or Drive
Staff ask questions in Teams or Slack and get answers from company documents they are allowed to see, with sources.
Read the blueprint
Supply chain
Forecasting on your sales data
Demand forecasts built from ERP and sales history, written back to planning screens buyers already use.
Read the blueprint
Platform
A central AI gateway
One controlled route to OpenAI, Anthropic or Google for every team: single sign-on, usage limits, logging and cost reporting.
Workflow
Agents across several systems
Agents that read an email, check the ERP, update the CRM and ask a person to approve, with every step logged.
:: Step by step
How to integrate AI into your business, step by step
The order matters more than the model. This is the sequence we follow for every integration, from the first process to a company-wide rollout.
01
Pick one process and one owner
Start where people lose the most time to reading, typing or searching, and name one person who owns the outcome. Company-wide ambitions start with one team proving the value.
02
Map the systems and data access
List every system the process touches, how each can be reached (API, database, file export) and who is allowed to see what. Permissions decide more of the design than the AI does.
03
Choose the integration pattern
Embedded in an existing tool, a central assistant, a document pipeline or an agent. Pick the one that changes people’s daily work the least while removing the most effort.
04
Set up security and governance
Single sign-on, role-based access that mirrors your existing permissions, masking of sensitive fields, approved AI providers and a log of every request and action.
05
Build and connect
Connect the AI to the systems through their APIs, add retrieval for documents, and keep the AI’s write access as narrow as the task allows.
06
Test with real users and real data
Run it with the team that owns the process on real cases, compare against the current way of working, and fix what they find before anyone else sees it.
07
Roll out team by team and measure
Expand one team at a time, track usage, accuracy, cost and time saved, and review the results with each team before moving to the next.
:: How it works
How we roll out AI across a company
Start small, prove value, then scale with the controls in place.
01
Map systems and risks
Which systems hold the data, who can see what, and which rules the AI must follow.
02
Pilot one department
One integration, one team, measured against today’s numbers.
03
Harden for scale
Security review, SSO, access control, monitoring and support processes.
04
Expand
New teams and use cases on the same foundation, so each one is faster and cheaper than the last.
:: Cost and timeline
How much does AI integration cost?
Typical prices for our projects, based on a senior team at $25 to $35 an hour. Rollouts are quoted in phases, each with its own price and success measures.
Pilot integration
$5k – $15k
4 to 6 weeks
AI connected to one or two systems for one team, with access control, logging and a measured comparison against today’s process.
Department rollout
$20k – $50k
2 to 4 months
Several processes or systems for a department, with governance, monitoring, training and support for the people using it.
Company-wide platform
$60k – $150k+
4 to 9 months
A shared AI gateway, reusable integrations, security and compliance controls, and a programme of use cases across teams.
What it costs to run
Running costs are mostly model usage plus hosting for the gateway, retrieval and monitoring. They scale with how many people use it and how often. A central gateway lets you see and cap spend per team, so costs never surprise finance.
What moves the price
Number and age of systems to connect
Security, compliance and data residency rules
How many teams and processes are in scope
Whether a central AI gateway is needed
Training and change management for staff
:: Governance
Enterprise AI governance, built in from day one
Security and compliance teams usually ask the same questions. These are the answers built into every integration we deliver.
Built into every integration
Single sign-on and role-based access
AI only sees data the user is already allowed to see
Sensitive fields masked before they reach the model
Enterprise model APIs that don’t train on your data
A log of every request, answer and action
Usage and cost reporting by team
Common mistakes we help you avoid
Staff pasting company data into personal AI accounts
One shared API key with no audit trail
AI with broader access than the people using it
Pilots that never get a security review
No owner for accuracy after launch
:: Why Sterling
Built for enterprise requirements
AI respects the permissions already set in your systems
Data stays in your cloud tenancy or Salesforce org
Full audit log of prompts, answers and actions
NDA before you share details
Experience across Salesforce, ERP and custom software in one team
:: Tools we use
Salesforce
Agentforce
MuleSoft
SAP
NetSuite
Microsoft Dynamics
SharePoint
Azure OpenAI
AWS Bedrock
Snowflake
Databricks
Okta
4.9
Average rating on Clutch · 350+ projects since 2019
:: Questions
Questions about enterprise AI
What are AI integration services?
Can you integrate ChatGPT with our internal systems?
How do you handle security and compliance?
How much does enterprise AI development cost?
Do you work with Agentforce?
What does an AI integration project involve?
How long does AI integration take?
Can AI respect our existing user permissions?
Do we need a central AI gateway?
Which systems can you integrate AI with?
How do we control AI costs across the company?
:: Related AI services
Other ways we can help
AI agent development
Agents that complete tasks inside Salesforce, ERP and email
AI consulting services
Plan before you build: readiness, strategy and proof of concept
AI software development
Custom AI products and internal tools, built to production
Generative AI development
LLM apps, RAG and fine-tuning on your data
Send us your brief. We’ll help turn it into reality
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