Choose the starting point that fits your problem. We define scope and evaluation together, then connect the required model, application, and infrastructure work. For agents and models, the path is build, evaluate, monitor, then deploy: release happens only after evaluation and monitoring are in place.
What we build
Five kinds of AI systems we design and deliver, each delivered under one of our services.
Illustrative architectures, not client deployments or performance claims.
Autonomous agents
Agents that plan multi-step work, call permitted tools, and check their own progress within defined limits.
Typical stages
Plan & tools
Approval gate
Evaluate
Monitor
Deploy
What we deliver
Planning and tool-use harness, scoped credentials, step budgets, and escalation to a person. An evaluation suite and trace monitoring come first; release follows once both pass.
How we evaluate
Task completion, tool authorization, behavior after failed steps, and when the agent stops or escalates, checked before release and monitored in production.
Agents embedded in recurring processes such as intake, triage, and document handling, with people approving consequential steps.
Typical stages
Trigger & act
Human review
Evaluate
Monitor
Deploy
What we deliver
Process mapping, system integrations, routing rules, review queues, and an audit trail for every action. Evaluation on real samples and monitoring dashboards precede deployment.
How we evaluate
Routing accuracy on real samples, exception handling, review load, and recovery from failed actions, tracked after release as well.
Spoken interfaces for support, intake, or internal tools, with a clear handoff to a person.
Typical stages
Speech in
Language model
Tools & data
Speech out
Handoff
What we deliver
Speech recognition and synthesis integration, turn-taking and interruption handling, tool access, transcripts with retention controls, and human handoff.
How we evaluate
Recognition accuracy on your vocabulary, task completion, response latency against an agreed budget, and handoff behavior.
Adapting a model to a domain when better context or prompting does not meet quality, cost, or latency needs.
Typical stages
Data curation
Adaptation
Held-out evaluation
Release gate
What we deliver
Dataset preparation, parameter-efficient fine-tuning with LoRA (low-rank adapters) or QLoRA (adapters trained on a quantized base model to reduce GPU memory), or full fine-tuning, where access and licensing permit. Baseline comparison and model packaging.
How we evaluate
Held-out quality against a simpler baseline, regressions, adapter versus full fine-tuning trade-offs, and serving cost.
Serving infrastructure for open-weight models when latency, throughput, or data-residency needs rule out a hosted API.
Typical stages
Model weights
Batching & KV cache
GPU serving
Routing & scaling
Monitoring
What we deliver
Serving-engine selection, quantization and batching configuration, multi-GPU layout, load testing that reports prefill and decode separately, autoscaling, and observability on cloud or private hardware.
How we evaluate
Prefill latency (time to first token) and decode latency (per token) at p95 and p97, plus throughput, measured on your workload. Also memory use, cost per request, and behavior under load and failure.
Multi-step work across documents and business systems, where automation needs clear boundaries.
What we deliver
Workflow design, retrieval, tool integrations, context and harness engineering, approval steps, and task traces. Before release: an evaluation suite built from real tasks, tracing and monitoring with alert thresholds, and a release gate. OpenClaw customization where appropriate.
How we evaluate
Completion and escalation behavior, source grounding, tool authorization, and recovery from failed actions. After release: drift, failure rate, escalation rate, and cost are monitored against the baseline.
Domain-specific quality, model size, or operating cost requirements that a general model may not meet.
What we deliver
Data preparation, training or fine-tuning pipelines (including LoRA and QLoRA adapters), model comparison, and inference optimization. Pre-training requires suitable data and compute; weight-level adaptation requires model access and licensing. Closed models depend on provider-supported options.
How we evaluate
Held-out task quality, baseline comparison, regressions, latency, memory, and training and serving costs.