AI, software & data services

Help staff find answers in approved documents and reference material. Connect a language model to relevant sources through retrieval-augmented generation (RAG), with source references where appropriate.

Example use case: an employee asks about a process and receives an answer linked to the relevant policy or guide.

Define which sources each user can access, how updates are handled, and when the assistant should say it does not have enough information. Test answers against representative questions.

Extract fields, classify documents, and prepare summaries from PDFs, scans, and other business material. Combine language or vision models with conventional processing and validation rules.

Example use case: an incoming document becomes a structured record, with missing or inconsistent fields flagged for review.

Agree on the output format, where it goes next, and which exceptions require a person to check them.

Connect an assistant to selected business tools so it can look up information, prepare a record, or help move a task forward. Define exactly what it can access and which actions need approval.

Example use case: an assistant gathers the details for a request, calls an approved business function, and passes the result back to the user.

Build checks around missing information, failed tool calls, and actions that require human confirmation.

Build applications that work with more than text. Use language and vision models to interpret documents or images, and audio processing to turn recordings into usable information.

Example use case: scanned pages become structured fields, or a transcript becomes a summary ready for review.

Test difficult inputs as well as routine examples, and define what the next person or system needs to receive.

Connect existing tools through APIs, internal applications, and data pipelines. Reduce repetitive copying, preparation, and follow-up so your team can spend more time on work that needs judgment.

Use conventional software where rules are clear, and AI where language or unstructured information makes it useful.

Many business applications can start with an existing foundation model, supported by prompts, retrieval, and software integrations. Fine-tuning is an option when the task, training data, and evaluation results justify it.

Compare approaches using your examples. Assess output quality, response time, operating cost, and the amount of review each result needs.

The goal is to choose an approach that fits the work and to understand its limits before scaling up.

Understand whether the data behind a decision is trustworthy. Review completeness, consistency, joins, definitions, and analytical methods to identify gaps and explain their impact.

Get findings tied to evidence, with practical recommendations and clear limits on what the data can support.

Use predictive models, forecasting, dashboards, and analysis to answer a defined business question. Start with the decision you need to make, then assess the available data and choose an appropriate method.

Keep assumptions, uncertainty, and data limitations visible so the results are useful to the people who act on them.

Consultation: clarify the problem, requirements, and a practical next step.

Focused pilot: test one workflow with representative examples and agreed success criteria.

Build or review: develop the application, connect it to your systems, or investigate an existing AI workflow that needs improvement.

Agree on deliverables, timing, budget, and maintenance responsibilities before work begins. Use the email link below to describe your project.