AI agents you can attest to
We build agents that execute tasks and integrate systems with deterministic tools, output validation, and grounding — the agent never acts on data it cannot verify.
The problem we solve
LLMs thrown into production without proper architecture hallucinate, act on data they never verified, and become unpredictable exactly when you need trust the most. The common blind spot is treating the agent as a creative black box. In production, what matters is not that the agent is clever — it is that it is reliable, verifiable, and auditable.
Most implementations stop at "an agent that calls a tool and hopes for the best." What's missing is the layer that separates a demo from a system: validation of every output, grounding in the real sources, human fallback when there's uncertainty, and a trail that lets you attest, afterward, why the agent did what it did.
How we build
It is not just a RAG — it is a harness around the model. We build agents with deterministic tools, output validation at every step, grounding (the agent only acts on data it can verify against the sources), and human fallback built in. Each agent has a clear scope, observable logs, and can be audited decision by decision. The model's intelligence is wrapped in an engineering layer that validates before acting.
When the agent hits uncertainty — ambiguous data, a decision outside the covered scope — it does not guess: it escalates for human review. That boundary is what separates an agent that helps from an agent that creates rework. This is what we mean by AI you can attest to: every action is traceable back to the source that justified it.
Model choice is part of the architecture, not a detail. We use Haiku for high-volume, low-cost tasks, Sonnet or GPT-4o for complex reasoning, and prioritize self-hosted models (Llama, Mistral) when privacy or cost is critical. We integrate via REST API, webhooks, databases, or queues — the agent adapts to your infrastructure, not the other way around.
What you get
An agent in production with a defined scope, auditable deterministic tools, grounding in your sources, and human fallback built in. Observability wired, the decision trail logged, and documentation that lets your team evolve the agent without depending on us.
This page describes a method, not a client case. Public example repositories of this verifiable-agent architecture are on the way. Want to discuss a concrete case? Tell us the scenario and we respond with feasibility within 24 business hours.
- Agent with clear scope and well-defined tools
- Output validation and deterministic fallback at each step
- Integration with your systems via API, webhook, database, or queue
- Automatic escalation of uncertainty to human review
- Observable logs and an audit trail of every decision
- Model choice calibrated to cost and privacy (Haiku, Sonnet, Llama, Mistral)
Investment ranges
Micro Project
PoC, institutional site, WhatsApp and small chatbots. Non-regulated sector, or your first AI project.
$8,000 – $20,000
- Delivery in weeks
- RAG + light harness
Small Project
Well-defined scope: targeted automation, lean MVP, focused integration.
$20,000 – $70,000
- Fixed scope
- Delivery in weeks
Medium Project
RAG chatbot, enterprise AI agent, SaaS MVP, performance execution.
$70,000 – $220,000
- Dedicated architecture
- Integrations
Large Project
Legacy modernization, system rewrite, multi-phase transformation.
From $230,000
- Multiple phases
- Dedicated team
Qualitative ranges. The exact figure comes out of Discovery, and is 100% credited toward the project.
FAQ
Which models do you use?
Depends on the case: Haiku for high-volume, low-cost tasks; Sonnet/GPT-4o for complex reasoning. We prioritize models that can be self-hosted (Llama, Mistral) when privacy or cost is critical.
Does it work with our existing systems?
Yes. We integrate via REST API, webhooks, databases, or queues. The agent adapts to your infrastructure, not the other way around.
How do you make sure the agent does not hallucinate?
Well-defined tools + output validation + deterministic fallback. The agent never acts on data it cannot verify — any uncertainty is escalated for human review.
How much does an agent cost?
It depends on the number of tools, integrations, and the reliability level required. We start with a diagnosis and, when scope justifies it, a Discovery Pack that estimates a qualitative range — credited if the project moves forward.