Business Process Automation
We build AI agents and intelligent assistants that extend your team's capabilities. From knowledge-based Q&A to task-executing agents, we engineer the AI capability around your requirements, data and guardrails.
The starting point
A knowledge-based assistant can retrieve and synthesize information from your documentation, making expertise accessible to everyone who needs it.
Agents can be built with permission layers and confirmation steps, so updates are proposed rather than executed blindly.
What the work includes
We define the task, the information sources, the actions permitted and the validation steps required. Model selection follows the requirements rather than the other way round. Agents are tested against representative inputs, edge cases and failure scenarios before deployment.
Explore the delivery processQuery your documentation, technical specs or historical records and get answers grounded in your actual information.
Agents that can perform permitted actions like scheduling, record updates or report generation, with validation checkpoints.
Present options, compare alternatives and highlight considerations based on your rules and historical decisions.
Coordinated agents where one specializes in research, another in validation, and a third in execution.
Permission boundaries, output validation, human confirmation steps and audit logs for every AI action.
An effective AI agent knows what it can do, what information it can access, and when to ask for human input. We define the permitted actions, validation rules and escalation path before model selection.
Knowledge assistants retrieve from your designated sources. Task-executing agents operate within bounded permissions. Decision support presents options without forcing a choice. Match the agent design to the level of autonomy the task requires.
Evaluate the agent on complete user tasks, including missing information, conflicting data and requests outside its scope. Those checks determine whether the agent is ready for wider deployment or needs tighter guardrails.
For agents that need to execute actions across systems, integration services connect the tools and records involved. Guardrails define which actions are permitted and which require confirmation.
We walk your current workflow with the people who run it and record where work stalls, duplicates or waits.
A fixed price and a dated plan, issued after discovery. Payment runs against milestones.
Review the proposed workflow, interfaces and data requirements before implementation. The design work follows the scope of the project.
Implement the agreed scope in short cycles, review the changes and test the behaviour with representative records and exceptions.
Test the required connections, confirm acceptance with your team and release according to the agreed rollout plan.
The team that built it handles updates, integrations and support calls after go-live.
Projects here typically run between USD 7,500 and USD 15,000 or above, depending on scope. We quote a fixed price after discovery and bill against milestones.
Fits when: Scope is clear and agreed after discovery
Billing: Milestone payments against a dated plan
Fits when: Scope will evolve, and you want capacity reserved
Billing: Monthly, per named engineer
Fits when: The system is live and needs changes and cover
Billing: Monthly, with an agreed response window
We compare candidate models on representative inputs from your work, measuring accuracy, response time and cost. Frontier models are one option for complex reasoning. Specialized or smaller models may fit simpler tasks better and run cheaper.
Only with the permissions and validation steps you define. Agents can propose changes for human approval, execute within bounded parameters, or require two-factor confirmation for critical updates. We design the guardrails alongside the capability.
The agent should acknowledge uncertainty rather than guess. We build confidence thresholds, fallback paths to human experts, and validation checks. Every response can be logged for review.
We use retrieval-augmented generation (RAG) to pull from your designated knowledge base before generating responses. The agent cites sources, and you control which documents it can access.
Yes. One agent retrieves information, another validates it, a third executes based on the result. Multi-agent systems can handle complex workflows where a single model would struggle with the full sequence.
AI workflow automation done carefully: AI interprets, business rules decide, and a human reviews before anything updates a system of record.
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Bring the task, the information it needs and the actions it should take. We will assess model options, guardrails and validation steps.