CaseMark – Embedded Engineering for Legal AI
CaseMark needed experienced engineering capacity that could integrate directly into its existing product team and contribute without slowing delivery down. higroup joined as an embedded engineering partner, working inside CaseMark’s existing processes, architecture, and infrastructure to help develop and scale a production legal AI platform.
Joining a fast-moving AI product without becoming an external vendor
CaseMark was already building a technically demanding product where legal documents, AI processing, asynchronous workloads, cloud infrastructure, and security all had to work reliably together.
The challenge for us was not to arrive with a new process or rebuild what already worked. We needed to understand the existing architecture quickly, integrate with the internal team, and start contributing meaningful production work while maintaining the standards required for a legal AI platform.
CaseMark’s product handles workflows such as document ingestion, grounded AI research, structured summaries, matter collaboration, and secure delivery, making reliability and traceability particularly important.
Embed first. Improve from inside.
We worked as part of CaseMark’s existing engineering organisation rather than as a separate outsourced team.
That meant adopting their tooling, architecture, communication, and delivery practices first, then contributing engineering capacity where it created the most value. Our engineers worked directly with the CaseMark team across backend development, AI workloads, cloud infrastructure, scaling, and production reliability.
The goal was simple: add capability without adding another layer to manage.
Embedded engineering across product, AI, and cloud
Our work became part of the wider CaseMark engineering effort, helping strengthen the systems behind a production legal AI platform.
- /Backend engineering — We contributed to backend services and asynchronous processing using Python, gRPC, and Celery, supporting workloads that need to process large amounts of legal information reliably.
- /AI infrastructure — We worked with AI services including OpenAI and AWS Bedrock as part of the wider processing architecture supporting AI-powered legal workflows.
- /Scalable cloud workloads — Containerized workloads ran on AWS EKS, with KEDA used to scale processing capacity around demand rather than keeping unnecessary resources running continuously.
- /Production engineering — Buildkite, Sentry, monitoring, and supporting infrastructure practices helped keep deployment and production behaviour visible as the platform evolved.
Together, the engagement gave CaseMark additional engineering capacity without creating a separate delivery silo. higroup engineers worked inside the existing team and contributed directly to the same product, infrastructure, and production environment.
“They put in the work to identify our problems and come back with phenomenally creative solutions for us to implement”
— Scott Kveton, Co-Founder & CEO - Casemark
The hard parts
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Common questions
What did higroup do for CaseMark?+
higroup provided embedded engineering capacity, working directly inside CaseMark’s existing product and engineering team across backend development, AI workloads, cloud infrastructure, and production reliability.
Was this a traditional outsourced software project?+
No. This engagement was closer to staff augmentation / embedded engineering. Our engineers joined CaseMark’s existing processes and technical environment rather than operating as a separate delivery team.
How did higroup integrate with the CaseMark team?+
We adopted CaseMark’s existing tooling, architecture, delivery practices, and communication structure, allowing our engineers to contribute alongside the internal team rather than adding another management layer.
What technical areas did higroup work on?+
Our work included Python backend services, gRPC, asynchronous Celery workloads, AI integrations, AWS infrastructure, Kubernetes, autoscaling, CI/CD, and observability.
Why was scalability important for the project?+
CaseMark processes document-heavy and AI-driven workloads that can vary significantly in volume. Infrastructure therefore needed to scale processing capacity efficiently while remaining observable and reliable.
What made the CaseMark engagement different?+
The value was not only additional engineering hours. higroup engineers became part of the existing product organisation, taking ownership of production work while adapting to CaseMark’s architecture and ways of working.