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.
For workflows where a person reads the same screen, queries the same data, and clicks the same tools all day.
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 market is real and the budgets are committed. The gap is execution: most pilots stall before they touch a live system.
Up from under 5% in 2025. Agents stop being a separate product and become a feature inside the software teams already run.
The first agent is almost never customer-facing. It is the internal queue someone works by hand all day, triage, lookups, drafting.
Another 39% are still experimenting. Adoption headlines mix pilots with deployment, and the two are not the same thing.
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 proofs-of-concept never reach widescale deployment.
IDC
Of agentic AI projects forecast to be cancelled by 2027, mostly from unclear value, runaway cost, and weak governance.
Gartner
Of adopters that do reach production report measurable productivity gains.
2025 surveys
Cost reduction reported on the workflows that are genuinely a fit for autonomous execution.
2025 surveys
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.
A chatbot answers. An agent acts. It retrieves from your data, decides what to do, calls your APIs to do it, and escalates to a person when it is unsure. The hard engineering is in the acting: authenticating against real systems, staying inside limits, and logging every action. That is the part we build.
Guardrails and evals. We set hard limits on what the agent is allowed to do (for example, escalate any refund over a threshold), and we run an eval suite that catches regressions before they ship. Every action is logged, so when something does go wrong you can see exactly what happened and why.
When the rules are fixed and the volume is predictable, a deterministic script is cheaper, faster, and easier to audit than an agent. We test fit before we build. If a script is the right tool, we tell you, and we will build that instead. Agents earn their cost on judgment-heavy, high-volume work, not on tasks a flowchart already covers.
Per-run cost depends on how much retrieval and reasoning the task needs, and we model it before we build so there are no surprises at scale. We instrument cost per run from day 1 and tune against real traffic. The workflows worth automating are the ones where the run cost is a fraction of the staff time it replaces.
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.
Scope an agent build
Claude API 
RAG 
Voyage embeddings 
pgvector 
MCP 
Python