AI model integration
Connect suitable model APIs to existing products and workflows, with prompts, validation and clear fallback behaviour.
Pact / Expertise · AI integration · Automation · APIs
Useful AI starts with a business workflow.
Pact Integration helps teams identify repetitive tasks where AI and software automation can reduce manual work. The work may involve document extraction, business-data classification, support triage, CRM enrichment or approval-driven workflows—always with attention to accuracy and oversight.
What we can help with
From the right architecture to useful day-to-day software.
n8n and workflow orchestration
Coordinate triggers, APIs, approval steps, notifications and audit trails across systems.
Document and data processing
Structure information from forms, emails or documents for review and downstream business use.
Human-in-the-loop controls
Route sensitive decisions to people, log changes and make exceptions visible instead of assuming every AI output is correct.
Working together
Scope, build, review and improve.
First we map inputs, outputs, exceptions and the business impact of a wrong answer. Then we prototype against representative data, measure error cases, define the approval path, and estimate usage and operating costs before production rollout.
AI integration / implementation notes
Can AI be added to an existing business application?
Yes. Existing web applications can call AI services through a controlled backend API without rewriting the whole product. The right first step is to define one workflow, its data boundaries, its acceptable error rate, and where a person must approve an output.
A practical implementation path
- Map the workflowDocument the trigger, source systems, fields allowed to leave each system, expected output, and the cost of a wrong answer.
- Choose the integration boundaryUse a server-side ASP.NET Core service, Node.js API, or managed workflow to hold credentials, call the model, and validate responses against a schema.
- Keep people in controlPlace review states and audit logs around consequential actions such as customer messages, stock adjustments, document approvals, or changes to records.
- Test before scalingEvaluate the workflow with representative cases, monitor failed or uncertain outputs, set usage limits, and make retries and fallbacks explicit.
Typical use cases to evaluate
Invoice and document intake
Extract structured fields from supplier PDFs, validate totals and identifiers, then present exceptions for review before data enters the ERP.
CRM and lead qualification
Summarise incoming enquiries, classify project requirements, and draft a response for human approval without exposing private customer data unnecessarily.
Inventory and operations assistance
Explain stock exceptions and surface suggested actions using permission-checked business data; require approval before write operations.
These are illustrative service capabilities and design patterns, not claims that a named client project has already been delivered.
Questions people ask
Clear answers, before the first call.
Can AI be integrated into an existing ASP.NET Core application?
Yes. An ASP.NET Core backend can securely call an AI model API and return validated results to your existing MVC, Angular or React interface. The application can retain its authentication and SQL Server or Oracle data model while adding scoped capabilities such as document classification, knowledge search, summarisation or workflow assistance. API keys remain on the server; outputs need validation and error handling.
What is a practical first AI automation project for a business?
Start with a repeatable task that has measurable inputs and outcomes, such as reviewing inbound enquiries, extracting fields from purchase orders or routing support requests. Run a limited pilot using representative examples. Track manual time, correction rate, processing cost and exceptional cases before automating actions that change business data.
Do AI workflows need human approval?
Not every workflow does. Low-risk formatting or internal draft suggestions may run automatically. Actions with financial, regulatory, personal-data or customer impact should generally have approval checkpoints, role-based permissions, audit logs and a way to reverse or retry an operation safely.
Can n8n connect AI models to our CRM or ERP?
Often yes, when supported connectors or secured APIs are available. An n8n flow might receive a webhook, call an API, prepare a structured model request, request approval, and update a CRM. Production flows need protected credentials, idempotency checks, rate-limit handling, retries, observability and defined ownership.
How do you protect business data when integrating AI?
The integration design should minimise shared fields, keep API credentials server-side, enforce user permissions, and define storage and retention rules. For sensitive records, evaluate vendor data-handling terms, regional requirements, redaction and whether a retrieval layer is preferable to sending complete documents to a model.
What affects the cost and timeline of an AI integration?
The main drivers are data readiness, number of systems and APIs, approval rules, model usage, quality testing, security review and deployment constraints. A small proof of concept and a production integration are not equivalent. Scope a narrow workflow and estimate implementation and ongoing inference costs before committing to a rollout.
Explore more
Connected disciplines, one clear goal.
From our engineering journal
How an AI-assisted approval workflow fits into an existing .NET application.
A practical reference architecture connecting ASP.NET Core, SQL Server, n8n and a model API, with manual review and audit trails. This is an illustrative technical walkthrough, not a client case study.
Let's build thoughtfully
Bring the problem. We’ll help define the right solution.
Tell us what needs to change, which systems are involved and what success looks like. We can discuss the approach and an appropriate next step.
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