Most agent pilots never leave the demo. We build Claude-based agents with the auth, audit logging, and guardrails to run against real systems. And we tell you which workflows should not be an agent at all.
It reasons over your data and acts inside your tools , then hands off to a person where it matters.
For teams with a high-volume workflow someone reads the same screen for, all day.
High volumes of repetitive, low-tier inquiries consume expensive human labor.
An agent that resolves routine tickets from your own documentation and escalates complex issues to staff.
Employees waste hours extracting data from unstructured documents and keying it into databases.
An agent that runs alongside your database, parsing incoming PDFs and injecting structured data through secure APIs.
Pulling actionable insight from large datasets needs data scientists they cannot afford.
A custom RAG system that queries your private data to give management immediate, context-aware answers.
The agent retrieves, reasons, acts, and routes back to your systems on every run.
It pulls grounded context, decides what to do, calls your APIs, and loops or escalates when it is unsure, with every step logged and access-controlled.
The budgets are committed and the demos work. The teams that win are the ones building the layer that survives real, messy data.
78% of enterprises run active AI pilots, but only 14% reach production scale. The gap is evaluation, integration, and trust, not the model.
The market is moving past isolated chatbots toward agents embedded in real workflows, with guardrails, cost-per-run tracking, and the Model Context Protocol.
Retrieval-augmented generation, vector databases, and the Model Context Protocol are becoming the default way to ground agents and let them act safely.
An agent demos well in an afternoon. Then it has to authenticate against real systems, log every action, stay inside cost limits, and hand off cleanly when unsure. That layer is where projects die, and it is the layer we build first.
Of AI agent projects die in the pilot phase and never reach production.
Hypersense, 2026
Of organizations deploying generative AI see zero measurable return on investment.
MIT Project NANDA, 2026
Of agent scaling failures trace to poor legacy integration and a lack of monitoring.
Digital Applied, 2026
Of agentic AI projects forecast to be cancelled by 2027, from unclear costs and absent risk controls.
Gartner, 2025
The reasoning is one part. These are the parts that decide whether it survives contact with production.
Your docs, tickets, and records, embedded with Voyage and served from pgvector, so the agent answers from your reality, not the model's training data.
Function calling and MCP connectors so the agent reads and writes in your real systems: CRM, ticketing, orders, billing.
Hard limits on what the agent can do, plus an eval suite that catches regressions before they reach a user, not after.
Role-based access, a logged record of every action, and traces of cost and latency per run. The layer that passes a security review.
Clear escalation rules so the agent does the routine 80% and a person gets a clean handoff on the rest, with full context.
We map the workflow and its volume. If a deterministic script is the right tool, we say so before you spend on an agent.
We ground the agent in your data and wire it to your systems, with a working build you can test against early.
We add the production layer: hard limits, an eval suite, role-based access, and a logged record of every action.
We track cost per run, escalation rate, and error rate, and we tune against real traffic instead of guesses.
Two builds where the work was in the parts that do not demo: data, integration, and trust.
Why it is relevant: a platform where every action was traceable and access-controlled, the same operational layer an agent needs to pass a security review.
Why it is relevant: a real-world scheduling and marketplace product, the kind of live system an agent has to read and write against without breaking.
If your SLED scope calls for AI automation, RAG over a document corpus, or agent deployment, we build it behind the prime. The boundary is fixed on purpose.
NDA-first, subcontract-only. We work behind the prime. We do not pursue prime contracts and we never face the agency.
Capability over claims. Claude API, RAG architectures, Voyage embeddings, and workflow automation (n8n, Make.com), mapped to your bid's technical scope.
Governance built in. Auth, audit logging, and guardrails are part of every agent we ship, the controls a procurement security review asks for.
We mitigate hallucination by using strict retrieval-augmented generation to restrict the agent's knowledge to your approved documents, not the model's training data. On top of that we add guardrails that force the agent to escalate any query it cannot answer with confidence to a person, rather than guess.
If the task involves completely predictable, structured data, you absolutely should use a standard script. It is cheaper, faster, and easier to audit. Agents are only worth it when you need to process unstructured text, natural language, or variable inputs. We test that fit before we build, and we will tell you when a script is the right answer.
We establish strict cost-per-run tracking and caching from day 1, so the agent only calls expensive models when a task genuinely needs complex reasoning. You see the cost per run, and we tune it against real traffic instead of guessing.
Yes, but it operates as a parallel system. The agent never holds direct database access. It requests data through an API gateway that enforces your existing user permission rules, so it can only ever see what the requesting user is already allowed to see.
Tell us the workflow someone works by hand all day. We will tell you whether an agent fits, and what it takes to ship it safely.
Claude API 
RAG 
Voyage embeddings 
pgvector 
MCP 
Python 
Your APIs