Pick the piece you need.
A prompt template library. A repo of reference agents. A GitHub Actions workflow for evaluation. A code-review agent tuned to your standards. Use what helps. Skip what doesn't.
scoped · flat-fee · delivered in days
Agentic Solutions
Agents you'd be willing to put your name on.
Multi-agent systems on your data, with named human owners, evaluation in place from day one, and a security model your team already recognizes. Cloud, hybrid, or on-prem.
Agentic solutions
From writing software to directing it.
Agentic development isn't the same SDLC at higher speed. The shape changes: humans set intent and review outcomes, agents do the drafting and the routine work in between. We help you make that transition deliberately, and instrument it so you can tell whether it's actually working.
Traditional SDLC
Humans write, machines run.
- Requirements documents handed off.
- Engineers translate intent to code by hand.
- Reviews catch defects after the fact.
- Velocity bound by headcount.
- Value measured in tickets closed.
Agentic solutions
Humans direct, agents draft.
- Intent expressed in templates and prompts, versioned in GitHub.
- Agents draft, refactor, and test inside guardrails.
- Reviews happen at the gate, with automation underneath.
- Velocity bound by what's worth doing.
- Value measured in outcomes shipped.
An AI factory shaped to your environment.
We assess your stack, your data boundaries, and your review culture. Then we install the templates, tools, and agents that make sense for your team. You own the line. We tune it, write the manual, and keep updating it as the work changes.
install · train · keep tuningInside your development lifecycle.
We embed in your sprints, pair with your engineers, run the evaluation harness, and own the agent fleet alongside your leads. Outcomes-on-the-board accountability.
embedded · outcome-bound · alongside your team
Human-in-the-loop
Agents act. People decide.
Autonomy is a dial. The point of an agent is to add to a person's reach. Every agent we ship has a named human owner, an explicit point of review, and a clear rule for when it stops and asks. We design the loop on purpose, then we instrument it so the loop holds under load.
Human-in-the-loop is not a checkbox at the end of the project. It's an operating discipline: the right person, at the right gate, with the right context, making the call that matters.
The agentic development loop, with humans on the gate.
Every agent we ship runs this loop. Five phases, one runtime, and a human-in-the-loop gate that can escalate when stakes are high or confidence is low.
agent flow
escalate to human
The four gates we design
Gate 01
Intent.
A human writes the goal, the constraints, and the data the agent is allowed to touch. The prompt is a contract, versioned in the repo, signed by a name.
Gate 02
Action.
High-stakes actions (money out, records changed, citizens contacted) require an explicit human approval. Low-stakes work runs, with a sample held back for review.
Gate 03
Exception.
When the agent's confidence drops, the data falls outside policy, or a tool returns something unexpected, the loop closes. The work pauses and a human picks it up where the agent left off.
Gate 04
Review.
Every agent has a weekly review, every action has an audit trail, and every owner has a dashboard showing what their agent did and where it asked for help.
Security & architecture
Sensitive data stays where it belongs.
Most AI failures aren't model failures. They're data-handling and access failures. We design every engagement around three questions that have to be answered before anything ships: what data can the agent touch, where does the model run, and who is accountable when it acts.
Public-grade cloud, with the right boundaries.
For work that doesn't touch regulated or proprietary data: drafting, code, research synthesis, public-facing content. Hosted on enterprise or FedRAMP-aligned infrastructure (Azure, AWS, Google Cloud, plus their public-sector tiers) with tenant isolation and data-residency controls.
- Frontier models, no hardware footprint.
- Pay-as-you-go, scale on demand.
- Allow-listed providers; no shadow AI.
An AI gateway that routes by sensitivity.
The pattern most clients land on. An AI gateway inspects the request, classifies the data, and routes to the smallest model that can do the job: local for regulated, cloud for everything else. Every call is logged.
- Role-based authorization on every request.
- Audit trail with input, output, and model used.
- Sensitivity policy enforced at the gateway.
Local models for the data that can't leave.
For HR records, financials, customer PII, and anything else covered by regulation or contract. Open-weight models (Llama, Mistral, Gemma) running on your hardware or in a sovereign tenant. Data never crosses the perimeter.
- From a single workstation to a small GPU rack.
- Same agent code as the cloud path; different engine.
- Air-gap-friendly when the policy demands it.
Three requests, one gateway, three destinations.
The hybrid pattern in motion: every request is classified at the gateway, then routed to the smallest model that's allowed to handle it.
Public → cloud
Drafts, public records, research synthesis. Routed to a frontier model on FedRAMP-aligned infra.
Internal → tenant
Vendor docs, drafts, redacted material. Routed to a model in your private tenant with residency controls.
Restricted → on-prem
Regulated, sensitive, or proprietary data: HR, finance, customer records, anything covered by HIPAA, CJIS, FERPA, or contract. Routed to an open-weight model on your hardware. Data never leaves the perimeter.
Defense in depth, applied to agents.
Agents introduce new failure modes (prompt injection, over-broad tool access, memory poisoning) on top of the ones your security team already manages. We layer the controls so a single mistake never reaches the data.
Governance
Audit and oversight
Every action logged. Every decision explainable. A trail your auditors recognize.
Network
Segmentation
On-prem, private cloud, and public cloud kept in separate lanes with explicit crossings.
Identity
Authenticated agents
Every agent has an identity. Every call is signed. No anonymous tool use.
Authorization
Least-privilege by default
Citizens see their own data. Staff see their department. Agents see only what their role unlocks.
Code
Sandboxed development
Agents are built and validated in isolation before they touch production. Version-controlled, peer-reviewed, rollback-ready.
The factory
Become the team that can vibe‑code safely.
The hard part of agentic AI isn't the agent. It's the line around it: the QA gates, review loops, evaluation harness, and change-management trail that let your developers ship contextual software quickly without losing the audit.
We install that line. The output isn't a deliverable; it's a capability. Your team becomes a small, equipped factory: one that can take an idea from a request to a working internal tool in days, with the seatbelts on.
1
Intent & context
Capture the request, the data, and the policy boundary.
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2
Generate & review
Agent drafts; human reviews; tests run; trail is recorded.
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3
Gate & ship
Security, accessibility, and policy gates before any merge.
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4
Operate & learn
Telemetry, incident path, and a monthly evaluation review.