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.

Depending on the task, the software may suggest an answer, prepare a draft or carry out a defined action. A workflow can combine rules and AI. We agree where a person must review the result and keep human approval for consequential actions.

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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

    Consequential actions require human approval. We log actions and agree the fallback before launch.

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.

01

Automate everyday work

Reduce repetitive handling and give your team a clear point to review the result.

Illustrative workflow

Incoming invoice

Extract details

Supplier
Amount
Due date

Your team reviews

Then send to accounting

Explore the use cases

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.

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.

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.

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.

02

Improve customer experiences

Help people find the right information, products and next steps.

Illustrative workflow
Find something that fits
Match relevant product information

Relevant options. The customer chooses.

Explore the use cases

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.

AI-powered product features

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

03

Predict and plan

Turn historical data into signals your team can investigate and act on.

Illustrative workflow
Demand signalsForecast
Historical dataPossible range

Review the signal

Your team decides the next step

Explore the use cases

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.

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 agreed fallback when something goes wrong.

  • Answers with no sources

    Confident replies that cannot be checked.

  • Another tool to learn

    A separate app nobody opens.

With Dexcode

  • Assessed before it is built

    Expected benefits, implementation effort and running costs assessed before the build.

  • Logged, owned, with a fallback

    Logged actions, a named owner, an off switch and an agreed route for uncertain or failed results.

  • 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.

Planning your release

How scope shapes your timeline

The number of workflows, data readiness, evaluation and system access shape an AI delivery schedule.

One assistant or automation
5–10 weeks
One use case and a principal system or data source, including evaluation, human approval points and supervised rollout.
One prediction task
6–10 weeks
A focused forecasting, fraud or risk task with suitable historical data and an agreed evaluation approach.
Connected AI workflows
Planned in phases
Several use cases, data sources or systems need a separate estimate. We establish evaluation and oversight for each workflow before expanding.

Indicative ranges with suitable data and system access. We confirm data readiness, scope and schedule during planning.

Explore our process
Process

How an AI integration project runs

The four stages cover discovery through evaluation and initial handover. Plan for 5–10 weeks for a focused assistant or automation, or 6–10 weeks for one prediction task with suitable historical data. We confirm scope, data readiness and dependencies during planning. Ongoing support is scoped separately.

Full process
Stage 01

Discover

Assistant / automation: 1–2 weeksPrediction model: 1–2 weeks

Choose one useful AI task and check whether the data can support it.

  • Inventory manual processes and recurring decisions with the people doing them
  • Compare expected benefit, implementation and running costs, data quality, access and the consequences of a wrong result
  • Choose a manageable first scope with a measurable outcome, or recommend a simpler next step
What you get
  • Ranked opportunity list
  • Scope and estimate
Stage 02

Design

Assistant / automation: 1–2 weeksPrediction model: 1–2 weeks

Decide where AI acts and where a person decides.

  • Data sources, permissions and privacy
  • Approval points and fallbacks
  • Agree evaluation examples, acceptable results and when the system must ask for help.
What you get
  • Solution design
  • Evaluation plan
  • Milestone schedule
Stage 03

Build

Assistant / automation: 2–4 weeksPrediction model: 3–4 weeks

Build a focused solution and test it against representative examples.

  • Integration with the systems involved
  • Evaluation against real examples
  • Logging and audit from day one
What you get
  • Working assistant, automation or prediction model
  • Evaluation results
Stage 04

Run in parallel, then improve

Assistant / automation: 1–2 weeksPrediction model: 1–2 weeks

Initial rollout and handover. Ongoing support is agreed separately.

Compare results with your team before relying on the system, then hand over its operation.

  • Run alongside your team, review disagreements and refine the system or route uncertain cases to a person
  • Hand over with a named owner, an off switch and a runbook
  • Agree who monitors quality and cost, and scope any further integration separately
  • Model and dependency updates under the agreed support arrangement
What you get
  • Documented handover
  • Off switch and runbook
  • Documented support responsibilities
  • 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.

Orchestration
LangGraphNode.jsTypeScriptPythonQueues & workers
Model gateway
LiteLLM
Models & APIs
Anthropic ClaudeOpenAIOpen-weight models where required
Data & retrieval
MongoDBPostgreSQLpgvectorPineconeElasticsearch
Machine learning
pandasscikit-learnXGBoost
Guardrails
Evaluation suitesAudit loggingHuman-approval stepsScoped permissions
FAQ

Questions about this service

Sometimes. We compare what your existing tools can do with the workflow, integrations, permissions and volume you need. If configuration or a ready-made product is enough, we recommend it. Custom work makes sense when those options leave an important gap or their ongoing cost is difficult to justify.

Ready to talk about AI integration?

Tell us what you are trying to achieve. In a free 30-minute strategy call, we discuss whether this service fits and what the next step would involve. Any discovery work starts with an agreed proposal.

Book a strategy call