TRUSTED BY TEAMS
/// About
Sodio is an AI agent development company building single-task and multi-agent systems that act on real business systems rather than answering questions about them.
An agent needs tool access, scoped permissions, approval gates, trace logging and step-level evaluation before it can safely touch production data which is where most of the engineering effort goes.
We build agents on LangGraph, CrewAI and custom orchestration, routed across Claude, GPT and open-weight models by task complexity and cost.
/// AGENT VS CHATBOT
An Agent Acts. A Chatbot Answers.
A chatbot responds to a question. An agent is given a goal, plans the steps to reach it, calls the tools and systems needed to carry them out, checks whether the result is right and adapts when it is not.
The difference shows up in the engineering, not the demo. A chatbot needs a good prompt and grounded retrieval. An agent needs credentials scoped to least privilege, an allowlist of actions it may take, spend and step limits so a reasoning loop cannot run away, approval gates before anything irreversible, and trace logging detailed enough to reconstruct a run afterwards.
That is why agent projects stall at the pilot stage. The reasoning works. The permissions, error handling and observability are missing, so nobody is willing to point it at production.
///WHAT WE BUILD
AI Agent Development Services
AI Agent Consulting
Which workflows are genuinely agent-shaped and which are better served by a script or a simple LLM call. We rule out the bad candidates first, with a costed roadmap for the rest.
Single-Task Agents
Agents that own one workflow end to end: ticket triage, invoice processing, lead research, document classification. Narrow scope, measurable outcome, fastest route to production.
Multi-Agent Systems
Orchestrated agents with specialised roles, shared state and a supervising planner. Used where one workflow spans several systems or requires distinct reasoning steps.
Tool & System Integration
Giving agents real capability through your CRM, ERP, ticketing, database and internal APIs. Includes permission scoping, rate limiting and rollback for every action an agent can take.
Guardrails & Human-in-the-Loop
Approval gates, action allowlists, spend limits, audit logging and escalation paths. This is what makes an agent safe to point at production systems rather than a sandbox.
Agent Evaluation & Monitoring
Trace logging, step-level evaluation, regression testing and cost tracking per run. Without it you cannot tell whether a prompt change improved the agent or broke it.
Which workflows are genuinely agent-shaped and which are better served by a script or a simple LLM call. We rule out the bad candidates first, with a costed roadmap for the rest.
Agents that own one workflow end to end: ticket triage, invoice processing, lead research, document classification. Narrow scope, measurable outcome, fastest route to production.
Orchestrated agents with specialised roles, shared state and a supervising planner. Used where one workflow spans several systems or requires distinct reasoning steps.
Giving agents real capability through your CRM, ERP, ticketing, database and internal APIs. Includes permission scoping, rate limiting and rollback for every action an agent can take.
Approval gates, action allowlists, spend limits, audit logging and escalation paths. This is what makes an agent safe to point at production systems rather than a sandbox.
Trace logging, step-level evaluation, regression testing and cost tracking per run. Without it you cannot tell whether a prompt change improved the agent or broke it.
///TECHNOLOGY STACK
Our AI Agent Tech Stack
/// USE CASES BY FUNCTION
Workflows That Suit an AI Agent
Customer support
Ticket triage and routing, order lookup and resolution across systems, refund processing with approval gates, escalation detection
Sales
Lead research and enrichment, CRM hygiene and data reconciliation, meeting prep briefs, proposal drafting from past deals
Finance & operations
Invoice processing and three-way matching, expense policy checks, vendor onboarding, month-end reconciliation
Internal IT
Access request handling, onboarding and offboarding workflows, log triage, runbook execution with approval steps
Recruitment
CV screening against role criteria, interview scheduling across calendars, candidate research, pipeline reporting
Compliance
Document review against policy, regulatory change monitoring, audit evidence collection, exception reporting
/// HOW WE SCOPE A BUILD
How We Scope an Agent Build
Workflow assessment
We map the candidate workflows and test each against three questions: is there a clear success condition, can the agent reach the systems it needs, and what is the cost of a wrong action. Workflows that fail these get ruled out here.
Free solution architecture
Agent topology, tool and integration map, permission model, guardrails, evaluation approach, infrastructure and a costed delivery plan including estimated running cost per workflow. Yours to keep either way.
Prototype with traces
A working agent on one workflow against your real systems, in a sandboxed environment, with trace logging and step-level evaluation from day one. You see every decision it makes.
Production rollout
Scoped credentials, approval gates, spend limits, monitoring and escalation paths. Rolled out on one workflow first, then extended once it has proven stable under real load.
/// FAQ
Frequently Asked Questions
A chatbot responds. An agent acts. Given a goal, an agent plans a sequence of steps, calls tools and systems to carry them out, checks the result and adapts. A chatbot answers a question about an invoice; an agent retrieves it, validates the line items, flags the discrepancy and raises a ticket. The engineering difference is that agents need tool access, permissions, error handling and audit logging.
Workflows with several steps, a clear success condition and a human currently doing repetitive coordination across systems. Ticket triage, document processing, research and reporting, and data reconciliation are common. If a workflow is a single deterministic step, a script is cheaper and more reliable. We rule those out during assessment rather than building them.
Through constraint rather than trust. Agents get explicit action allowlists, scoped credentials with least privilege, spend and rate limits, and approval gates before any irreversible action. Every step is logged so a run can be reconstructed afterwards. For higher-risk workflows the agent proposes and a human approves.
A single-task agent against well-documented systems can reach a working prototype in a few weeks. Production readiness takes longer, because permissions, evaluation, monitoring and failure handling are where the real work sits. Multi-agent systems and poorly documented internal APIs extend the timeline. We size this during the free solution architecture.
Cost is driven by run volume, how many model calls each run makes, and model choice. Agents are more expensive per task than a single LLM call because they reason across multiple steps. Routing simple steps to cheaper models, caching, and capping steps per run all matter. We estimate running cost at your expected volume before you commit to a build.
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/// GET STARTED
Start With One Workflow
Tell us the workflow that eats the most manual coordination and which systems it touches. We will prepare a free solution architecture covering agent design, integrations, permission model and estimated running cost, so you can judge the approach before committing to a build.