AI INTEGRATION

AI that removes repetitive work, with a person still in charge

We put AI to work reading documents, drafting replies and classifying requests inside the tools you already use. It also helps with decisions that depend on your data, with human review where it matters.

What AI integration covers

Automate repetitive tasks

Take manual steps out of the processes that run your business.

  • Document extraction

    Pull the fields your team re-types from invoices, receipts, forms and PDFs, and flag exceptions for review.

  • Classification and routing

    Sort requests, tickets and enquiries to the right person or queue.

  • Drafting and summarising

    First drafts of replies, reports and notes for a person to check.

  • Human approval steps

    Anything consequential waits for a person, is logged and reversible.

Improve customer experience

Assistants, search and suggestions grounded in your own documents and data.

  • Internal knowledge assistants

    Staff ask questions over policies, manuals and past cases, with links to the original sources.

  • Customer-facing assistants

    Answer common questions around the clock, connected to your systems, with handover to your team when needed.

  • Search over your content

    Find the right document, product or record by describing it.

  • AI features in your product

    Intelligent search, content generation and recommendations inside the software you already sell.

Predict from your data

Forecasts and early warnings from the history your systems already hold, checked by your team before anyone acts on them.

  • Demand and stock forecasting

    Anticipate shortages and plan replenishment from sales, inventory and logistics data.

  • Fraud and risk flagging

    Suspicious transactions, likely payment delays and supplier disruptions surfaced for your team to investigate.

  • Predictive maintenance

    Early signs of equipment failure from sensor and maintenance records, so repairs are scheduled, not forced.

  • Recommendations and segments

    Products people are likely to want and audiences likely to respond, based on their history.

Use cases

Common use cases for AI and ML development

Put AI to work on your business challenges. We build tailored solutions that automate repetitive tasks, improve customer experiences and help your team make informed decisions.

Customer support automation

Answer common questions, route requests and help customers around the clock with assistants connected to your business systems, with handover to your team when needed.

Document processing and data extraction

Extract and organise information from invoices, receipts, forms and PDFs. Reduce manual data entry, flag exceptions for review and send validated information into your systems.

Internal knowledge assistants

Help your team find answers across company documents, policies and manuals. Scattered information becomes accessible answers with links to the original sources.

Inbox triage

Enquiries classified, drafted and routed to the right person or queue, with a person approving anything that matters.

Report generation

Recurring reports assembled from your data, ready for a person to check and send.

Personalised product recommendations

Help customers discover relevant products through recommendations based on their preferences, browsing activity and purchase history.

Marketing optimisation

Identify customer segments, personalise content and predict which audiences are most likely to respond to your campaigns.

Fraud detection

Flag suspicious transactions and unusual account activity in real time, so your team can investigate potential fraud and respond faster.

Risk prediction and monitoring

Use historical data to identify likely payment delays, supplier disruptions and other business risks, so your team can prioritise action.

Predictive maintenance

Identify early signs of equipment failure from sensor and maintenance data, helping your team schedule repairs and reduce unplanned downtime.

Supply chain and inventory optimisation

Forecast demand, anticipate stock shortages and support replenishment and delivery planning using your sales, inventory and logistics data.

AI-powered product features

Add intelligent search, content generation, recommendations and task automation to your software, helping users accomplish more within your application.

Our approach

What changes when it is built properly

Without Dexcode

  • The demo that never ships

    Impressive in a meeting, abandoned after the first real edge case.

  • Nobody knows what it did

    Actions with no log, no owner and no way to undo.

  • Answers with no sources

    Confident replies that cannot be checked.

  • Another tool to learn

    A separate app nobody opens.

With Dexcode

  • Priced before it is built

    Hours saved and error cost measured first.

  • Logged, owned, reversible

    Every action recorded; a named owner and an off switch.

  • Grounded in your data

    Answers cite the document they came from.

  • Inside your existing tools

    Built into the inbox, CRM or portal your team already uses.

Process

How an AI integration project runs

The same four stages as every Dexcode project, tailored to AI work. The ranges are typical for a first project: an assistant or automation usually reaches handover in 8–14 weeks, a forecasting or risk model in 12–20. The exact plan is agreed at the end of planning.

Stage 01
Typically 1–2 weeks

Discover

Find the work worth automating.

  • Inventory manual processes and recurring decisions with the people doing them
  • Score each by hours, error rate, the data available and effort
  • Choose the first process
What you get
  • Ranked opportunity list
  • Scope and estimate
Stage 02
Typically 1–2 weeks, longer if data access waits on a vendor

Design

Decide where AI acts and where a person decides.

  • Data sources, permissions and privacy
  • Approval points and fallbacks
  • Success measures per process
What you get
  • Solution design
  • Evaluation plan
Stage 03
Typically 3–6 weeks; 6–10 for forecasting, fraud or risk models

Build

Smallest version that could work, inside your tools.

  • Integration with the systems involved
  • Evaluation against real examples
  • Logging and audit from day one
What you get
  • Working automation
  • Evaluation results
Stage 04
Typically 2–4 weeks in parallel, then ongoing

Run in parallel, then improve

Compare before you rely on it, then pick the next process.

  • Automation and a person run side by side; disagreements are fixed in the software
  • Hand over with a named owner, an off switch and a runbook
  • Monitor accuracy and cost, then extend to the next step in the queue
  • Model and dependency updates on a schedule
What you get
  • Documented handover
  • Off switch and runbook
  • Support agreement
  • Improvement backlog
Technology

What we build AI integration with

Chosen for longevity and hiring, not novelty. We explain any alternative where the choice matters for your project.

Models & APIs
Anthropic ClaudeOpenAIOpen-weight models where required
Retrieval, data & ML
pgvectorPineconeElasticsearchPostgreSQLpandasscikit-learnXGBoost
Orchestration
TypeScriptPythonLangGraphQueues & workers
Guardrails
Evaluation suitesAudit loggingHuman-approval stepsScoped permissions
FAQ

Frequently asked questions

By cost. We score each manual process on hours per week, error rate and effort to automate, and start with the one that saves the most with the fewest dependencies.

Ready to talk about AI integration?

Tell us what you are trying to achieve. We will say whether this is the right kind of work, what it would involve and what it would cost.