Overmind Technology Ltd
Overmind builds the training and supervision infrastructure for specialized AI agents. It turns production traces into datasets, evaluations, optimized changes and fine-tuned models that AI teams can own, deploy and self-host.
At a Glance
- AI engineering and product teams
- Enterprises running agents in production
- Legal
- Healthcare
- +2 more
AI Tools by Overmind Technology Ltd
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Overmind
Train Small Models From Agent Data
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Products & Services
Maps an agent's capabilities from its repository, ingests OpenTelemetry traces and supported provider telemetry, and lets teams inspect executions, spans, scores, prompts, tools and failures.
Converts production traces, uploaded files or API rows into versioned datasets for evaluation and training, with agent-assisted transformations, executable Python cells, provenance and quality checks.
Generates evaluators and eval sets, compares prompts and models against a baseline, supports human annotations and backtesting, and runs repository-level optimization that returns scored candidate diffs.
Fine-tunes open-weight models on customer data using LoRA or full fine-tuning, tracks experiments and metrics, benchmarks against an incumbent, and serves or exports the resulting weights.
Market Position
Overmind positions itself as the end-to-end ownership and improvement loop for production AI agents, rather than only an observability layer or a model API. Its own comparison pages frame LangSmith and Langfuse as observability/tracing alternatives whose loop ends at analysis or dataset export, OpenPipe as a production-request-to-cheaper-model precedent, and OpenAI fine-tuning as a hosted alternative; Overmind differentiates through repository-aware tracing, evaluation and optimization, customer-owned fine-tuned weights, open-source/self-hosted deployment and an OpenAI-compatible serving layer.
Leadership
Founders
Tyler Edwards
Co-founder and CEO. He spent eight years building AI systems for UK intelligence agencies including MI5, MI6 and GCHQ.
Akhat Rakishev
Co-founder and former CTO. He previously led machine-learning infrastructure work at Monzo and Lyst.
Sam Brunt
Co-founder and COO (described as CRO in the launch coverage). He previously helped scale commercial teams at Funding Circle, Pipe and Vertice.
Executive Team
Tyler Edwards
Co-Founder & CEO
Built AI systems for MI5, MI6 and GCHQ over eight years before founding Overmind.
Sam Brunt
Co-Founder & COO
Previously scaled commercial teams at Funding Circle, Pipe and Vertice.
Founding Story
The founders started Overmind after identifying a gap in AI security: model-level defenses cannot prevent an autonomous agent from drifting or behaving dangerously while interacting with live production systems. Their initial vision was a deployment-layer supervision system that observes agent interactions, enables intervention and learns from real operational behavior; the current product extends that loop into data preparation, evaluation, training and deployment of models the customer owns.
Business Model
Revenue Model
Hosted SaaS/platform subscriptions with usage or credit-based charges for training, inference, Agent Optimizer runs and Data Workshop chat; enterprise contracts add custom usage, onboarding, security and support. The open-source platform can also be self-hosted.
Pricing Tiers
For prototypes and demos; includes fair-use OTLP ingest, up to 10 optimization runs per month, unlimited eval runs, up to 2 training jobs and 2 deployments per month, 5 projects, Console/CLI/MCP and community Discord support, plus a $50 one-time starter credit.
For teams shipping agents in production; includes the Free features with unlimited optimization, training, deployment and projects, plus priority support.
Adds dedicated technical support, SSO/custom auth, encrypted credentials, custom onboarding/playbooks and unlimited seats and usage contracts.
Target Markets
- AI engineering and product teams
- Enterprises running agents in production
- Legal
- Healthcare
- Financial services and fintech
- Other regulated or high-risk industries
- Training smaller specialized models for production agent tasks
- Observing and diagnosing agent failures, tool calls and production drift
- Creating evaluation and regression suites from real agent traffic
- Optimizing prompts, tools and agent code before deployment
- Fine-tuning and serving models owned by the customer
- PII anonymization and privacy-preserving data preparation