AI Integration Services

An engagement identifies workflows where AI can help, chooses a build or buy approach, designs the connection to your systems, builds it, tests it with real data and users, and supports it afterward. Typical examples are summarizing support tickets inside the help desk, suggesting next steps in a CRM, extracting fields from documents into an ERP, or adding semantic search to a product.

If you have no clear use case yet, AI consulting services can help prioritize. If you need a model trained on your own data, that is custom machine learning development. Integration begins once you know what capability you want.

Hosted services are documented in vendor references such as the OpenAI API documentation (opens in a new tab), Claude documentation (opens in a new tab) and Azure AI documentation (opens in a new tab). Many teams start with the first pattern, then move to the second as usage matures. The glue between systems is usually API development.

Software vendors increasingly ship their own AI features inside CRMs, help desks and productivity tools. Check those first, because a built-in feature is the cheapest to adopt.

• Use the vendor feature when it handles your data and workflow acceptably and you accept its limits.
• Configure a platform service when you need control over prompts, data sources or outputs.
• Build custom when the AI behavior is specific to your business or sits inside your own product.

Whichever route you take, keep a thin layer of your own between your systems and the AI provider. That makes later switches possible and gives you one place to apply logging, redaction and rules.

Draw the path of data before writing code. For each AI feature, answer these questions in a one-page diagram:

1. What goes in? Which fields, documents or messages are sent, and are all of them needed?
2. Who is asking? Does the AI see only what that user is already allowed to see?
3. Where does it run? Which provider, region and contract terms apply, and is data kept or used for training?
4. What comes out? Is the result a suggestion a person confirms, or an action taken automatically?
5. Where is it stored? Are prompts and outputs logged, for how long, and who can read them?

Redacting identifiers before sending text out, and restoring them afterward, is a common technique that lowers exposure. The ICO guidance on AI and data protection (opens in a new tab) stresses data minimization and transparency, which this exercise puts into practice.

AI services are probabilistic and sometimes unavailable. Your workflow should still work when they are not.

• Graceful fallback. If the call fails or times out, the screen continues as it did before the feature existed.
• Human confirmation for outputs that affect customers, money or records. Show the suggestion, let the user edit and accept.
• Confidence and validation. Check that outputs match the expected format, ranges and allowed values before saving.
• Rate and cost limits so a loop or spike cannot create a surprise bill.
• Versioning. Pin model versions where possible and re-test when a provider updates one, because behavior can change.
• Monitoring. Track errors, latency, user edits and rejections, which show whether the feature really helps.

Integration widens what AI can reach, so security design matters.

• Least privilege. Give the AI connection only the data and actions the feature needs, using service accounts and scoped tokens.
• Defend against injection. Treat text from emails, documents and web pages as untrusted. The OWASP Top 10 for LLM applications (opens in a new tab) lists prompt injection and excessive agency among the key risks.
• Vendor review. Check the provider’s security documentation, data retention and subprocessor terms.
• Accountability. Name an owner for each AI feature and keep a record of its purpose, data and testing.
• Staff communication. Tell people what the feature does and when to distrust it.

The NIST AI Risk Management Framework (opens in a new tab) suggests structuring this work around governing, mapping, measuring and managing risks, and cloud providers publish their own principles, such as AWS Responsible AI (opens in a new tab).

Illustrative scenarios, not Kramate results.

• B2B sales. A CRM panel drafts call summaries and suggested follow-ups, and the rep edits before saving.
• Distribution and logistics. Supplier emails and delivery documents are read and matched to purchase orders in the ERP, with exceptions queued for a buyer.
• Healthcare and legal operations. Staff search internal policies and past documents, with permissions enforced and no free-form legal or clinical advice.
• SaaS products. A software company adds semantic search and an assistant to its own product, calling hosted models through a shared gateway.

• USA. Requirements vary by state and sector, with HIPAA for health data and consumer privacy laws such as California’s. Contract terms on provider data use are a key negotiating point.
• UK. UK GDPR rules on lawful basis, transparency and international transfers apply when personal data goes to an AI provider.
• Australia. The Privacy Act 1988 and the Australian Privacy Principles cover disclosure of personal information to overseas providers.
• Singapore. The Personal Data Protection Act applies, and the national Model AI Governance Framework offers practical guidance.

Providers differ in available hosting regions, so check this early if data residency is a requirement. Obligations also arise under the EU AI Act (opens in a new tab) for systems used with EU residents. Confirm with legal counsel.

• Number of systems to connect and the quality of their interfaces.
• Data preparation, such as cleaning fields or building a search index.
• Provider usage fees, which scale with volume and input length.
• Security review, redaction and permission work.
• Testing effort and user training.
• Monitoring and updates when providers change models.

Run a small pilot on one workflow with a named owner, measure edits and time saved against your own baseline, then expand. Fixed-price suits a defined pilot, a dedicated team suits a rolling backlog of AI features, and hourly work suits investigation.

1. Understands your existing systems and asks about their interfaces and limits.
2. Maps data flow and permissions before choosing a provider.
3. Designs fallbacks and human review rather than promising perfection.
4. Supports several providers and keeps a switchable layer.
5. Plans monitoring and ownership after launch.
6. Transfers code and documentation with all intellectual property to you.

Kramate can lead delivery or embed engineers in your team to work beside your system owners. You get a dedicated project manager, daily updates on Slack or Teams, and a review at the end of each two-week round, plus an NDA before detailed discussions and full transfer of intellectual property. Access follows least-privilege practice. Details are in our delivery approach.

• OpenAI API documentation (opens in a new tab)OpenAI
• Claude documentation (opens in a new tab)Anthropic
• Azure AI documentation (opens in a new tab)Microsoft
• Guidance on AI and data protection (opens in a new tab)ICO UK
• OWASP Top 10 for Large Language Model Applications (opens in a new tab)OWASP
• AI Risk Management Framework (opens in a new tab)NIST
• AWS Responsible AI (opens in a new tab)Amazon Web Services
• EU AI Act (opens in a new tab)Future of Life Institute