Build AI products around real legal work.

I build the product around the AI task: user interface, accounts, backend, stored work and deployment. Users can review results and continue their work in the same application.

Under the hood: Application APIs, authentication and data storage connect to the model layer, with loading, error and recovery states.

I add search, drafting, classification or assistance to the product your users already know. The feature follows existing permissions and fits the surrounding workflow.

Under the hood: I connect existing APIs and account context, isolate the new behaviour and plan a release that can be rolled back.

I build conversational tools that help people ask questions, prepare work and turn free text into usable records. The interface makes review and correction part of the task.

Under the hood: Versioned prompts, managed conversation context and validated response formats keep model behaviour inspectable and testable.

I connect answers to approved company knowledge, with references people can inspect. When the available material cannot answer the question, the system makes that gap visible.

Under the hood: Retrieval-augmented generation (RAG) combines permission-aware document ingestion, indexing, retrieval and source checks; content updates are part of the design.

I turn documents into information your team can review and use: summaries, extracted fields and records linked to their source material. Unclear or missing values go to review.

Under the hood: Parsing, model extraction and field validation feed your application, retaining source references and routing exceptions to a person.

I build agents that gather context, use approved tools and prepare the next step in a defined workflow. You set the allowed actions and where a person must approve.

Under the hood: Tool permissions, step limits, validated inputs and execution records bound the agent’s actions and support investigation when a step fails.

I connect incoming work to the next operational step: classify a request, prepare a record, route it for review and update the agreed system. AI handles the parts that need interpretation.

Under the hood: API and event integrations coordinate jobs, retries and duplicate prevention, with human decision points for consequential actions.

I build tools for producing text, images or structured content with controls for the person using them. Users can provide context, compare results, edit and approve what they keep.

Under the hood: Generation APIs connect to templates, format checks and review steps; model and asset handling follow the project’s data requirements.

I define useful and unacceptable results, then test the system against representative tasks and difficult cases. Operational signals help your maintainer spot failures and changes in behaviour.

Under the hood: Repeatable evaluation cases compare prompt, model and retrieval changes; traces track failures, response time and usage costs.

I review an existing AI build and address the gaps preventing a dependable release. That can mean correcting retrieval, fixing failed jobs, tightening access or making failures recoverable.

Under the hood: I reproduce failures, review application and tool boundaries, add regression checks and document remaining risks and release conditions.