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AI Integration Services for Existing Software and Workflows

We add AI capability to the CRM, helpdesk, ERP or internal tool you already run, without replacing what already works.

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Code in your repository from day one
A working demo every two weeks
Senior engineers do the work
How we work

Most companies asking for AI already have the software; they need a capability added to it. A drafting assistant inside the helpdesk your agents use every day. Search over the account history sitting in Salesforce, without exporting it anywhere new. An invoice-reading step in front of the ERP your finance team already relies on. That's a different job from building a new AI product, and it calls for a different starting question: which vendor and model fit your data handling requirements, and how does the feature sit inside software your team didn't choose to replace.

We start with the systems you already run and the APIs they expose, then decide where an LLM belongs in that flow: as a suggestion a person approves, an extraction step that fills a form, or a search layer over data you're not moving anywhere. Vendor selection is part of the work, not an afterthought: OpenAI, Anthropic, Azure OpenAI and open-weight options differ on cost, latency, data retention terms and where the data physically sits, and the right answer changes with what the data is.

If what you actually need is a new AI-native product (an agent, a fine-tuned model, a computer vision system), that's a build project: see AI development. This page covers wiring AI capability into systems that already exist.

What we deliver

AI Integration services

  • AI Inside Existing Applications

    Copilots embedded directly in the CRM, helpdesk or ERP interface your team already uses daily, so a drafted reply, summary or suggestion appears where the work already happens instead of in a separate browser tab.

  • Document Extraction Pipelines

    Invoices, contracts, claims forms and applications get read and structured automatically, then written into the system of record you already use, with a review step for anything below a confidence threshold.

  • Vendor and Model Selection

    A comparison of OpenAI, Anthropic, Azure OpenAI and open-weight options against your actual requirements: cost per request, latency, data retention terms and whether the vendor trains on your data by default.

  • Data Privacy for Third-Party Models

    Redaction of sensitive fields before they reach an external API, regional hosting where required, and contract terms reviewed against your existing compliance obligations before any integration goes live.

  • Workflow Copilots

    Drafting, summarizing and triaging built into the tools your team already opens each day: ticket summaries in the helpdesk, deal notes in the CRM, first-pass responses that a person still approves.

  • Search and Q&A Over Existing Systems

    Natural-language search across records already stored in your CRM, wiki or ticketing system, without duplicating that data into a separate database you now have to keep in sync.

  • Platform Integration

    Direct integration with the APIs of the systems you run: Salesforce, HubSpot, Odoo and similar platforms, so the AI feature reads and writes through the same permission model your team already has.

You receive

Deliverables

  • A working AI feature live inside your existing application
  • A vendor and model comparison memo with a documented recommendation
  • Data flow and privacy documentation for the integration
  • Integration code committed to your existing repository
  • Usage and cost monitoring for the new feature
  • A rollback plan if a vendor or model needs to change later

How we work

Our ai integration process

  1. Systems and Data Audit

    We map which systems are involved, what data each one holds, and which fields are sensitive enough to need redaction or special handling before any vendor is chosen.

  2. Vendor and Model Shortlist

    A short list of model providers evaluated against your data handling requirements and budget, with a clear recommendation and the trade-offs behind it.

  3. Integration Design and Privacy Review

    We design exactly where the AI step sits in the existing workflow and review the data flow against your compliance obligations before writing integration code.

  4. Build Inside the Existing Application

    The feature is built into the application your team already uses, through its existing APIs, rather than as a separate tool people have to remember to open.

  5. Pilot With a Limited Group

    A small group of real users tests the feature against real cases, with every low-confidence result logged for review before wider rollout.

  6. Rollout and Monitoring

    The feature rolls out to the full team with usage and cost monitoring in place, so adoption and spend are visible from week one.

Choosing a Model Vendor for Regulated Data

Not every model provider handles data the same way. Some retain requests for abuse monitoring by default, some offer contractual guarantees against training on your data, and some let you choose the region where processing happens. For health, financial or other regulated data, the vendor comparison has to happen before anyone designs the integration.

We compare providers against your specific obligations rather than a generic checklist: what a healthcare client needs from a vendor agreement is different from what a fintech client needs, and both are different from a company with no regulatory requirement at all but a reasonable expectation of privacy from its own customers.

Where AI Belongs Inside an Existing Tool

The easiest integration to get wrong is one that adds an AI feature as a bolt-on screen nobody opens. The features that get used sit inside the workflow that already exists: a draft reply that appears in the same window an agent is already typing in, a summary that shows up on the ticket itself.

That means the integration work is as much about where in the interface the feature lives as it is about which model answers the request.

  • Suggestions appear where the work already happens, not in a separate tool
  • A human approves or edits before anything goes to a customer
  • Confidence thresholds decide what gets auto-applied versus flagged for review

Extracting Structured Data From Unstructured Documents

Invoices, applications and claims forms rarely arrive in a consistent format, even from the same sender. We build extraction pipelines that handle that variation: reading the document, mapping fields to your existing system's schema, and flagging anything below a confidence threshold for a person to check rather than posting it silently.

The goal is fewer manual entries, not zero human review. A well-built pipeline removes the repetitive cases and routes the genuinely unusual ones to someone who can make the judgment call.

Technology

Technologies we use

Industries

AI Integration by industry

Solutions

Systems we build with this service

Budget

What drives cost and timeline

  • Number of systems touched

    A feature that reads from one CRM is simpler than one that reads from a CRM and writes into a separate billing or ERP system.

  • Data sensitivity

    Health, financial or other regulated data usually requires redaction and vendor terms review that add time before any model call happens.

  • Vendor contract terms

    Some vendors require enterprise agreements for data retention guarantees, which affects both cost and how quickly the integration can go live.

  • Document or request volume

    Extraction pipelines and search features scale in infrastructure cost with volume, independent of the initial build cost.

  • Custom UI work

    A feature that reuses the existing application's interface costs less than one that needs new screens built inside it.

Get a project estimateSee engagement models

Compliance

Standards we design and build to

None of these is a certification or attestation held by this agency. They are the standards we design and build client systems to, so your own audit or assessment starts from a sound base.

  • GDPR / UK GDPR

    Personal data handled on a lawful basis, with data minimisation and a documented retention policy.

  • HIPAA safeguards

    Administrative, physical and technical safeguards applied where a system touches protected health information.

  • PCI DSS scope reduction

    Card payment flows tokenised through a certified processor so cardholder data never touches your systems.

  • SOC 2 readiness

    Access control, change management and audit logging built in from day one, ready for a client's own SOC 2 audit.

  • WCAG 2.2 AA

    Keyboard navigation, colour contrast and screen-reader labelling checked against the AA success criteria.

  • OWASP ASVS

    Authentication, session handling and input validation reviewed against the Application Security Verification Standard.

We list certifications only when they are real and current. See our security practices →

FAQ

AI Integration FAQs

Can you add AI to our existing Salesforce or HubSpot instance without migrating data?

Yes. We integrate through the platform's existing APIs, so the AI feature reads and writes records where they already live rather than copying them into a separate database. The account structure and permission model you already have stay in place.

Which AI vendor should we use if we handle patient or financial data?

It depends on the specific regulation you're working under and the vendor's current contract terms, which change over time. We compare options against your actual obligations, including data retention, training use and hosting region, and document the trade-offs before recommending one.

How do you stop an AI copilot from seeing data a user shouldn't have access to?

The integration runs through the same permission model your existing system already enforces. If a user can't see a record in the CRM today, the AI feature built on top of it doesn't get access to that record either, because it queries through the same API with the same credentials.

Can AI read PDF invoices and post them into our existing accounting system automatically?

Yes, with a confidence threshold: extracted fields above the threshold post automatically, and anything below it gets flagged for a person to check before it's entered. That balance keeps the manual review focused on the genuinely unusual cases instead of every invoice.

How is this different from building a custom AI product?

Integration adds a capability to software you already run and keep running as-is. Building a product means designing a new system, such as an agent or a fine-tuned model, from scratch. If your CRM, helpdesk or ERP already does most of what you need, integration is usually the faster and cheaper path.

Do you support Azure OpenAI or private model hosting instead of the public API?

Yes. Azure OpenAI, other enterprise-hosted options and self-hosted open-weight models are all on the table when the standard public API doesn't meet your data handling or compliance requirements, and we'll flag the cost and latency trade-offs of each before you commit.

Insights

Guides for planning this work

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