Services
AI development and integration services
We help you decide what to build, then build it, connect it to your existing software, and keep it running. We also prepare training data and deploy AI on your own hardware.
The application,
its integrations,
and where it runs.
Hire us for a complete system or one defined part of it. Design, build, deployment, and who looks after the system afterward are covered in one delivery plan.
- Applications, APIs, data pipelines, and AI services
- Cloud, on-premises, or hybrid deployment
- Evaluation, rollout, recovery, and maintenance planning
Architecture and AI strategy
AI strategy and readiness
We review proposed AI projects against your team’s actual work, available data, and likely costs, then recommend which one, if any, to pursue first.
AI governance and architecture
We design access rules, approval steps, and release checks for AI applications, covering where data goes, what the system can change, and who handles failures.
Data and analytics strategy
We trace reporting problems to their sources and plan the repairs, from conflicting calculations to missing records and the manual work behind each report.
AI transformation programs
We coordinate AI and software projects that share data, applications, or teams. The plan sets the order of releases and what each team must resolve first.
Private equity AI assessment
We measure one portfolio-company workflow, compare AI with simpler options, and specify a pilot you can test before funding a wider rollout.
Applications and integration
AI software development
We build AI applications for document review, search, drafting, and other tasks. The work includes the interface, data connections, testing, and deployment.
Custom software and AI integration
We build custom software and extend existing applications, adding LLM integration where AI helps. Work can include APIs, portals, or replacement components.
On-premises AI deployments
Use AI without sending your business data to an outside AI service. We build assistants, document tools, and agent workflows that run on your own hardware. We connect them to your systems, test them before release, and support them as agreed.
Internal tools and operational software
We build internal tools for requests, approvals, and shared records. They can replace spreadsheet trackers and connect systems your staff update by hand.
Agents and workflow automation
We automate recurring tasks such as sorting requests, collecting records, and preparing updates. Rules control what can change and when a person must review it.
LLM and knowledge systems
We build internal search and RAG assistants that answer questions from your own documents. Each answer cites its sources and respects document permissions.
Rapid software prototyping
We build a working prototype so your team can try an idea before funding the full application. It tests the part most likely to change the decision.
WebMCP modernization
WebMCP gives compatible browser agents a structured way to use your website, web application, or digital platform. We connect its search, forms, and approved actions to the software you already run.
AI automation for portfolio companies
We build AI document processing and workflow automation for portfolio companies, with staff review before updates reach the systems they already use.
AI integration for portfolio software
We repair fragile software in private equity portfolio companies, connect the systems of acquired businesses, and add AI features where they help.
Data and AI foundations
We clean and connect the records a report, application, or model depends on, from duplicate IDs to undocumented calculations and access gaps.
Data engineering and integration
We build data pipelines between your systems, with documented field mappings, checks for missing or duplicate records, and recovery for failed runs.
Business intelligence and decision systems
We build Power BI and other business intelligence reports on agreed calculations, so your team can trace each figure back to its source records.
Data science and predictive analytics
We build forecasting and classification models for planning and operations, and test them against simpler methods on data held back from development.
Deployment and operations
AI evaluation and operations
We build tests from the tasks your AI application handles and the mistakes users run into, so you can compare changes in accuracy, response time, and cost.
Ongoing improvement
We maintain a development backlog from user feedback and recurring problems, then deliver tested updates on an agreed schedule.
Managed AI and data operations
We monitor and maintain named applications, integrations, and data pipelines. The agreement sets out support hours, response commitments, and what is excluded.
Training data and synthetic data
Custom training dataset development
We prepare and label training examples for your model from approved sources. Each release records where every example came from, which cases it covers, and what changed since the last version.
Pretraining data curation
We select, clean, and remove duplicates from text collections for pretraining. The mix of sources reflects your model, your permitted uses, and your compute budget.
Synthetic data generation services
We generate examples for the scenarios your existing data does not cover. We check each batch against the required schema and review it for the task it is meant to support.
Synthetic test data services
We create fictional records for application tests, staging environments, and demos. The files include relationships, expected outcomes, and deliberate error cases.
Fine-tuning and preference datasets
We prepare instruction, conversation, tool-use, and preference examples for fine-tuning. We review answers and formats, and keep evaluation cases separate from training data.
Data annotation and quality review
We define labeling rules, review annotated examples, and investigate disagreements. The release includes the label definitions and our quality findings.
Evaluation datasets and benchmarks
We build evaluation sets with reference answers or scoring rules for your application. Cases cover routine tasks, known failures, and exceptions that need separate review.
Dataset pipelines and governance
We automate dataset preparation and release, including quality checks, source history, and versioning. The pipeline comes with instructions for corrections and failed runs.