Practical AI for
existing business systems
Add useful AI features to existing products and business systems, from document search and summaries to assisted support and workflow automation. We keep the data, model, access, and review decisions clear.
An AI feature needs a defined job.
AI integration means connecting a model or machine-learning feature to the software and data a team already uses. It can support document search, summaries, classification, assisted replies, forecasting, or other narrowly defined tasks.
We help teams decide where AI is useful, what information it should see, how people will review the result, and how usage, cost, accuracy, and failures will be monitored.
Practical AI integration scenarios
We focus on use cases with a clear user, a reliable source of information, and a sensible way to check the result.
Language models
Natural Language Workflows
Connect a suitable language model to customer support, document review, summaries, drafting, or search. Model choice depends on the task, data, cost, and the provider terms the organisation can accept.
Prediction and classification
Predictive Algorithms
Use historical data to support classification, prioritisation, forecasting, or anomaly review. The data quality, evaluation method, and consequences of an incorrect result must be understood first.
Documents and media
Visual & Speech Extraction
Connect OCR, document extraction, translation, speech, or image review to an onboarding or internal workflow, with a clear review path when the source is incomplete or uncertain.
A careful architecture
for AI features
AI providers introduce variable response times, usage costs, context limits, and changing model behaviour. We design around those constraints instead of hiding them behind a demo.
1. Manage repeated work
Cache only the requests where a repeated answer is safe and useful. Keep cache lifetime, invalidation, and access rules explicit.
2. Retrieve approved context
RAG can retrieve relevant documents or records for an answer. Permissions, source quality, citations, and update behaviour are part of the design.
3. Set usage boundaries
Track requests and tokens, set sensible limits, and provide a useful response when a provider is unavailable or the budget is reached.
4. Make waiting understandable
Where a response takes time, show progress or stream it in a way that fits the user journey. Do not make a live interface promise more certainty than the model can provide.
Data handling for AI features
AI features can expose information to model providers, logs, prompts, and search indexes. We map those paths and agree practical controls before connecting sensitive business or customer data.
Sensitive information
Identify the information that should not be sent to a model or stored in a prompt. Depending on the workflow, that may involve masking, field-level access, redaction, or keeping the data in your own system.
Controlled context
Provide only the fields and documents the task requires. Keep permissions, source references, retention, and any restoration of redacted values under application control.
Provider terms
Review the model provider's data-use, logging, retention, region, and support terms before sending production information. We do not describe a provider policy as a project guarantee.
Private deployment options
Where the project justifies it, we can evaluate a private or self-hosted model. The decision should account for model quality, hardware, maintenance, access, cost, and the organisation's obligations.
Our AI discovery and
delivery process
We work with the people who understand the process and the team responsible for the software. Each step should leave a decision, test, or working result that can be reviewed.
Define the use case
Clarify the user, task, source information, expected result, review process, cost boundary, and the situations where the feature should not act.
Build and evaluate
Select the model or retrieval approach, connect approved data, and test representative examples with the people who will use and review the result.
Operate with care
Track usage, cost, response quality, failures, and user feedback. Add limits, fallbacks, and human review where the consequences of a wrong answer matter.
AI integration questions
Practical answers for teams considering AI in an existing product or business system.
Choose the task before choosing the model.
We scope AI features around a clear user, approved data, model and provider choices, evaluation, cost, and a safe operating process.
A defined task, the people who will use it, and the decision it should help them make.
Data access, evaluation, cost, human review, and what happens when the system is uncertain.
The people responsible for the workflow and the information it uses.
Operating guidance, evaluation notes, and ownership of the feature after release.
A useful first conversation
Bring the workflow, decision, or technical constraint that is creating the most friction. We can then decide whether discovery, a focused delivery phase, or a different next step makes sense.
Build an AI feature
that people can trust.
Start with the workflow, the information it uses, and the decision the feature should help someone make.