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Artificial Intelligence
Connect AI opportunities to reliable data, practical applications and measurable evaluation.
Discuss your challengeThe challenge
Make room for what comes next.
Interesting prototypes need a defined business purpose, an accountable owner and a clear reason to move beyond experimentation.
Fragmented knowledge makes useful answers difficult to verify, particularly when source documents change or carry different access permissions.
Accuracy, access and human oversight need explicit boundaries before generated output becomes part of a consequential workflow.
A promising demonstration may leave unanswered questions about integration, response time, operating cost and support responsibilities.
Teams need a practical way to compare AI-assisted work with their current process and decide when a simpler approach is sufficient.
Capabilities
The work that moves you forward.
Opportunity and readiness assessment
Start with a business task and the people responsible for it. Map the current process, available information, exceptions and decision points. Compare potential AI assistance with process changes or conventional automation, then define the evidence needed to justify a focused pilot.
Knowledge retrieval and search
Explore applications that help authorized users find information across approved sources. Design document ingestion, source references, retrieval permissions and content refresh together. Make uncertainty and missing information visible so a useful answer does not become an unsupported assertion.
AI application engineering
Connect model capabilities to a bounded user journey through application interfaces and existing business systems. Consider input validation, tool permissions, review queues and fallback behavior. Keep consequential actions under explicit authorization, with a clear path to continue the work when the AI component is unavailable.
Evaluation and human review
Build representative examples from the agreed task, including difficult questions, incomplete information and cases that should be declined. Review usefulness, groundedness and failure behavior with subject specialists. Define where people approve, correct or override an output and how their feedback informs subsequent changes.
Data and model boundaries
Examine which information a proposed application needs and where that information will be processed. Document provider choices, access rules, retention expectations and the permitted purpose. Plan how restricted records, untrusted content and changes to source permissions are handled throughout the workflow.
Operational readiness and improvement
Define observations that help owners understand application behavior after release, while limiting sensitive information in logs. Establish versioning, release evaluation, rollback and issue ownership. Review response quality, resource use and user feedback against agreed acceptance criteria before expanding the scope.
What takes shape
Useful outputs. Shared understanding.
Agree the scope and acceptance criteria together, then connect each deliverable to the way your teams work.
- An opportunity brief identifying the task, intended users, current baseline and the decision a pilot must inform.
- A data-readiness and permissions assessment with source owners, unresolved gaps and agreed restrictions.
- A pilot architecture describing application boundaries, integration dependencies, provider options and fallback paths.
- A representative evaluation set with review guidance, acceptance criteria and documented examples of unacceptable behavior.
- A tested application increment with source visibility, review controls and operational documentation appropriate to its scope.
- A release recommendation and improvement backlog connecting evaluation findings to accountable next steps.
Delivery approach
From the right question to a working solution.
Frame the decision
Work with business and technical owners to select a bounded task. Agree the current process, available evidence, excluded uses and a practical definition of usefulness before selecting a model or committing to an implementation.
Design the experiment
Inspect representative information and map access boundaries. Choose an approach that can answer the pilot question, prepare evaluation examples and agree the human review needed to make the experiment meaningful.
Build and evaluate
Develop reviewable increments and test them against ordinary, difficult and out-of-scope inputs. Discuss failures alongside successful outputs, then revise the workflow or narrow the scope where the evidence calls for it.
Decide and operationalize
Use the evaluation to decide whether to proceed, adjust or stop. For an accepted release, establish ownership, monitoring, fallback procedures and a process for reviewing changes to models, data and user needs.
Industry context
Built around your business.
Explore how these capabilities connect with the decisions and workflows in your industry.
Banking & Financial Services
Connect customer onboarding, service operations and platform modernization through clear information flows. Explore engineering possibilities for banking and financial services, with accountable people retaining ownership of financial decisions.
Insurance
Explore clearer policy servicing, document intake and claims administration. Connect information and handoffs while preserving human review, policy context and traceability throughout the service journey.
Life Sciences
Explore connected research information, controlled documentation and quality-workflow administration. Focus on source traceability, review responsibilities and repeatable engineering practices while keeping scientific interpretation with qualified owners.
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