AI and agentic systems built for production, not presentation.

Put AI into useful workflows

Start with a process, an accountable owner, and output somebody can check before choosing where a model belongs.

Adopting AI into Workflows

The need: Working out where AI genuinely takes work off people in a process you already run, then building it into that process.

Our approach: We start with the workflow and the data it touches, and we only take on tasks whose output somebody can check.

• Workflow assessment and use case selection
• Human in the loop and approval boundaries
• Integration with existing systems and queues
• Success criteria and acceptance thresholds
• Rollout staging and fallback behavior

Chat Workflows

The need: Asking questions of internal data and systems in plain language, and getting answers that show where they came from.

Our approach: Everything is grounded in retrievable sources and carries a citation, and the system is allowed to say it does not know.

• Grounded question answering over internal sources
• Citation and source attribution
• Conversation state and session design
• Escalation and handoff to a person
• Response quality monitoring

Build agentic systems with controlled access

Give agents explicit tools, scoped credentials, and human checkpoints so multi-step automation can act without gaining blanket access.

Agentic Workflows and Tool Use

The need: Systems that plan and carry out several steps against real tools, instead of answering once and stopping.

Our approach: Agents get the actions they are allowed and nothing else, with a human checkpoint wherever a mistake would be costly to undo.

• Multi-step planning and execution design
• Safe tool and action boundaries
• Human review checkpoints
• State, memory, and context management
• Failure handling and escalation paths

MCP Servers and Secure Context Access

The need: Giving AI assistants proper access to internal data, APIs, and workflows, so people stop pasting context in by hand.

Our approach: Every tool has its own credentials and logs each call, which means you can audit a single action and not just the connection.

• MCP server implementation and tool definition
• Scoped credentials and per-tool authorization
• Audit logging of tool invocation
• Internal API and workflow exposure

Cloud AI Platform Integration

The need: Building on the AI platforms already sitting in your cloud account, instead of standing something separate up beside them.

Our approach: Models are called inside the existing account and network, so the identity, logging, and perimeter rules you already run apply unchanged.

• AWS Bedrock: model access, Agents, Knowledge Bases, Guardrails
• Google Cloud Vertex AI: model access and grounding
• Vertex AI Agent Builder and the Agent Development Kit
• Agent Engine and Agentspace
• Vertex AI Vector Search
• Private connectivity, VPC endpoints, and VPC Service Controls
• IAM scoping and invocation logging

Make production AI measurable and governable

Ground outputs, gate releases with evaluation, trace runtime behavior, and surface policy failures and cost before they become incidents.

Retrieval and Evaluation

The need: The retrieval that grounds an answer, and the tests that prove it is grounded before anything ships.

Our approach: Quality is scored against a fixed set and gates the release, so a regression is caught by the pipeline and not by a customer.

• RAG architecture and chunking strategy
• Vector databases and hybrid retrieval
• Embedding selection and index design
• Faithfulness and grounding evaluation
• Regression evaluation suites and release gates

Guardrails, Auditability, and Cost Governance

The need: What keeps an AI system in production: limits on what it can do, a record of what it did, and the cost of each task.

Our approach: Policy checks and tracing sit in the runtime, and cost is tracked per task so a shift in usage shows up before the invoice does.

• Policy checks and content guardrails
• Audit trails for prompts, tools, and outputs
• Model routing and semantic caching
• Inference cost tracking per task
• Tracing and production observability

Related proof

Selected work and engineering blueprints relevant to this practice.

AI architecture review, 1 to 2 weeks

Pressure-test the workflow before scaling the demo.

We review the task, data access, tool boundaries, evaluation approach, failure paths, and operating cost. The result is a production architecture and a bounded pilot plan.

• Workflow, data, and tool-access architecture
• Evaluation, guardrail, and human-review plan
• Pilot scope, operating risks, and production path

Inspectable delivery

• Review packs: current state, risks, options, and accountable decisions.
• Decision logs: the chosen path, alternatives, and the reasoning behind it.
• Runbooks: operating, recovery, and handover paths for the team that owns the system.
• Checkpoint baselines: agreed signals that keep delivery progress visible.

Questions worth settling early

• Do you build MCP servers? Yes. We implement MCP servers and tool definitions with scoped authorization, invocation logs, and access to the internal APIs or workflows the assistant genuinely needs.
• Can you work inside our existing cloud account? Yes. We integrate with AWS Bedrock and Google Cloud Vertex AI so existing identity, network, logging, and perimeter controls remain part of the system.
• How do you decide whether an AI workflow is ready to ship? We agree a fixed evaluation set, acceptance thresholds, policy checks, failure handling, and human checkpoints. Quality regressions should fail the release rather than reach a user first.