A multi-LLM consensus pipeline that reviews contracts, reconciles model outputs, and routes to a human reviewer — agentic verification with an auditable trail.
Service — AI agent development
AI agent development means building software where an AI system plans, calls tools, and takes multi-step actions toward a goal — not just answering a single prompt. Godelian builds custom AI agents that retrieve data, call APIs and tools, make decisions, and hand off to a human when they should, with the logging, guardrails, and evaluation that keep them safe in production. Led by a founder and published AI researcher, we build agentic workflows on modern models, server-side, typically for $15–30k in weeks — scoped to a task that genuinely needs an agent, not agent-washing a form.
Start your build →An AI agent earns its name when a task needs planning, tool use, and several steps that can't be hard-coded — triaging a document, researching and drafting, orchestrating an API workflow. Most 'agent' projects don't need one; they need a well-built LLM feature. The first job is telling the two apart, because an agent you don't need is just a slower, more expensive way to be wrong.
When an agent is the right call, the engineering is all in the guardrails: tightly scoped tools, structured outputs, budget and step limits so it can't loop forever, and a clear hand-off to a human when confidence drops. Every action is logged, so you can see exactly what the agent did and why — the difference between a demo and something you'll trust in production.
Godelian builds these grounded in your data and evaluated against real cases before they go live, on the same stack as the rest of your product. The founder's published AI research and 15 years building these systems is the reason the guardrails are there from the start, not added after the first incident.
What clients say
“Andrew is in a different league. What started as a product concept turned into a masterclass in how technical projects and startups should run. Response time was instant, updates were proactive, and the final deployment was flawless.”
Relevant work
A multi-LLM consensus pipeline that reviews contracts, reconciles model outputs, and routes to a human reviewer — agentic verification with an auditable trail.
A document-to-decision workflow: intake, OCR, extraction, and risk scoring chained into a reviewable pipeline rather than a single opaque call.
A WhatsApp-native AI assistant that interprets messages and takes action, with the messaging integration and guardrails built around it.
AI agent development — questions
An AI agent is software that uses an LLM to plan and take multiple steps toward a goal — retrieving information, calling tools or APIs, making decisions, and acting — rather than just returning one answer. The value is autonomy on multi-step tasks; the risk is that autonomy without guardrails, which is why scoping, logging, and human hand-off matter.
You need an agent when the task genuinely requires planning and several tool-using steps that can't be scripted in advance. If the job is 'summarise this', 'answer from these docs', or 'extract these fields', a well-built LLM feature is cheaper, faster, and more reliable. Godelian will tell you which one your problem actually is.
With tightly scoped tools, structured outputs, step and budget limits so they can't loop or overspend, grounding in your data, evaluation against real cases before launch, full logging of every action, and a clear hand-off to a human when confidence is low. Guardrails are designed in from the start, not patched in after an incident.
A scoped agentic feature at Godelian typically costs $15–30k and ships in weeks. The cost is driven by the number of tools and integrations, the evaluation and guardrails required, and how much has to be grounded in your data — not by the model itself.
More in AI development
Currently taking on new builds
Start your build.One conversation with the founder. We'll map the shortest path to something you can put in front of real users.