AI Automation &AgenticSystems

Both and the choice should be driven by your latency, cost, compliance and capability requirements - not by our partnership preferences. We work fluently across Anthropic Claude, OpenAI GPT, Google Gemini and self-hosted open models (Llama, Mistral, Qwen) deployed on platforms like Modal, vLLM and Fireworks. For greenfield engagements, we make a model recommendation during Phase 1 based on your specific use case, with documented trade-offs against alternatives. For engagements where you already have a model provider relationship, we build against your existing stack and your existing procurement contracts. Where regulatory or compliance constraints require self-hosted inference, we deploy and operate that infrastructure end-to-end. The constant across every engagement is the evaluation harness - the model provider can change, but how we measure quality stays consistent. If you're considering switching providers mid-engagement (cost, capability, compliance reasons), we can run head-to-head evaluation on your real use case rather than generic benchmarks.

We specialise in operational automation: document workflows, retrieval systems, agentic tools with approvals, voice and chat interfaces with structured handoffs and integrations into CRM, ITSM and internal APIs. We avoid positioning AI as the sole decision-maker in regulated domains (clinical diagnosis, legal advice, lending approval) without attorney-, clinician-, or risk-approved human checkpoints - we augment those workflows with citations and structured outputs instead.

We scope data residency, redaction, logging policies and access controls in Phase 1. Retrieval and tool layers enforce least-privilege access; outputs can be masked or routed for review under your policy. For PHI-aligned workloads we align architecture to your BAA and security reviews - including hosted vs self-hosted inference trade-offs documented before build.

Yes - voice stacks with interruption handling and low-latency paths where your UX requires it; computer-use and browser automation with audit logging and human-in-the-loop gates on sensitive actions. Scope stays explicit about latency budgets, failure modes and escalation paths.

Golden test sets, automated eval in CI and production observability (latency, refusal rates, structured-output validation, retrieval grounding checks where applicable). Model or prompt changes ship only after they pass the harness - treating quality like any other regression surface.

Yes. We deploy vLLM/Ollama-style stacks, Modal/Replicate when hosted fits and VPC-bound inference when policy requires it - with cost, latency and maintenance trade-offs documented for your stakeholders.

Red-teaming against jailbreaks, injection via tool payloads and data-exfiltration patterns; tool allowlists; output validators; and operational limits on sensitive tools. Residual risk is documented - we do not promise zero misuse against a motivated adversary.

Yes - OAuth/service accounts, MCP servers where appropriate, rate limits, idempotency and audit logs. We design rollback and feature-flag cutovers so automation does not strand operators mid-flight.

Discovery through production-ready systems commonly runs 10-22 weeks depending on integration breadth, eval rigour and adversarial testing scope. Typical core team: lead ML/LLM engineer, integrations engineer, evaluation owner - scaled with workload.

Use case brief, representative data samples (or schema descriptions), systems to integrate, compliance constraints, latency and cost budgets and target go-live window. We respond within one business day with a scoped recommendation.