Agents that do real work, safely.
An agent is software that can read, reason, decide, and act across your systems. Done well, it removes whole categories of manual work. Done badly, it's an expensive demo. We build the first kind.
Where we see agents pay for themselves.
These are the patterns we build most often. Each one starts with your real documents, data, and systems, not a generic template.
Document intelligence
Invoices, purchase orders, contracts, permits, applications, and forms. The agent classifies each document, extracts the fields you need, validates against your systems, and routes exceptions to a person with the evidence attached.
- Structured extraction with confidence scores
- Cross-checks against ERP, CRM, or case data
- Audit trail for every decision
Support and service agents
Internal help desks, HR and IT service, and customer support. Grounded in your knowledge base and connected to ticketing and account systems, the agent resolves routine requests and drafts responses for everything else.
- Answers cite the source policy or article
- Escalation rules you define
- Deflection and satisfaction measured from day one
Analytics assistants
Business users ask questions in plain language and get answers from governed data, with the generated SQL, sources, and caveats visible. Built on a semantic layer so the numbers match your official reports.
- Semantic model prevents metric drift
- Row-level security respected
- Charts and narratives on demand
Back-office automation
Multi-step workflows across systems: vendor onboarding, reconciliation, compliance checks, month-end reporting, and records requests. The agent does the work and pauses for approval where policy requires.
- Deterministic steps where determinism matters
- Human approval gates
- Full replay of what happened and why
Engineering and IT agents
Agents that triage alerts, draft runbook actions, generate tests, review pull requests, and document legacy systems. Your engineers stay in control; the agent removes the toil.
- Integrated with Git, CI, and ticketing
- Scoped permissions per task
- Pairs with our agentic delivery practice
Domain copilots
Role-specific assistants for analysts, case workers, field technicians, or finance teams, embedded in the tools they already use and aware of their data and procedures.
- Embedded in Teams, Slack, or your apps
- Role-based context and permissions
- Feedback loop improves it every week
The whole system, not just the prompt.
A production agent is roughly one part model and nine parts engineering. We deliver all ten.
- Retrieval layer. Document pipelines, chunking, embeddings, hybrid search, and permission-aware indexes.
- Tool layer. Secure connectors to your systems, exposed through Model Context Protocol servers so any model can use them consistently.
- Orchestration. Planning, memory, multi-step execution, retries, and hand-offs between specialized agents.
- Evaluation. Golden datasets from your real cases, automated scoring, and regression checks on every change.
- Guardrails. Input and output policies, PII handling, allow-lists for actions, and approval gates.
- Operations. Tracing, cost and latency dashboards, model version control, and an on-call runbook.
Delivery model
- AI Readiness Sprint (2 weeks). Use-case scoring, data and integration review, risk and cost model, roadmap.
- Agent Pilot (4–6 weeks). Working agent on real data, evaluation baseline, go/no-go recommendation.
- Production Build (8–12 weeks). Hardened system with integrations, guardrails, monitoring, and training.
- Managed AI Operations. Ongoing evals, model updates, cost control, and feature iteration.
Model-agnostic. Cloud-native. Yours.
We pick the model for the job and design so you can switch when a better one arrives.
| Layer | What we typically use | Why |
|---|---|---|
| Models | Anthropic Claude, OpenAI, AWS Bedrock, open-weight models on your infrastructure | Best model per task; private hosting where data sensitivity demands it |
| Tools & integration | Model Context Protocol servers, REST and GraphQL APIs, event buses | One governed interface between agents and your systems |
| Orchestration | Agent SDKs, LangGraph, Step Functions, durable workflows | Reliable multi-step execution with retries and state |
| Knowledge | Vector search (OpenSearch, pgvector, Pinecone), hybrid retrieval, semantic layers | Grounded answers with citations and access control |
| Data foundation | Snowflake, Redshift, Databricks, S3 lakehouse, dbt, Airflow, Kafka | Trustworthy, fresh data for agents and analytics alike |
| Evaluation & observability | Custom eval harnesses, tracing, cost and latency dashboards | Know how it performs before and after every change |
| Platform | AWS, Terraform, containers and serverless, CI/CD | Secure, repeatable, cost-controlled infrastructure |
Access
Agents inherit the permissions of the user or service they act for. Nothing more.
Actions
Allow-lists define what an agent may do. High-impact actions require human approval.
Audit
Every input, retrieval, tool call, and output is logged and replayable.
Assurance
Evaluation sets, red-team prompts, and regression gates in the release pipeline.
Trust is a design requirement, not a policy document.
Leaders are right to worry about accuracy, data leakage, and runaway actions. We address each one in the architecture, then give you the evidence.
- Data stays in your cloud accounts and your model provider agreements.
- PII detection and redaction before anything reaches a model, where required.
- Clear ownership: a named business owner and a technical owner for every agent.
- Documentation suitable for auditors, security teams, and procurement.
Straight answers.
Where should we start with agents?
With a high-volume, rules-heavy process that has clear success criteria and a person who owns it today. Document handling and internal support are common first wins. Our two-week readiness sprint scores your candidates and recommends one.
How do you keep the agent from making things up?
Grounding, constraints, and measurement. Answers are retrieved from your sources and cited. Actions are limited to an allow-list. An evaluation set built from your real cases tells us the accuracy rate before launch and flags regressions afterward.
Which AI model do you use?
Whichever fits the task, the budget, and your data-residency needs. We design the system so the model is a swappable component. Most clients end up using more than one.
Will our data be used to train models?
No. We use enterprise APIs and cloud-hosted models under agreements that exclude training on your data, or open-weight models running inside your own environment.
What does it cost to run?
Model usage is usually a small fraction of the value created, but it must be controlled. We set budgets, cache aggressively, route simple requests to cheaper models, and report cost per task from the start.
Can our own team maintain it?
Yes, and that is the goal. Everything lives in your source control and cloud accounts, with documentation and training included. Many clients keep us on a light managed-operations retainer while their team ramps up.
Start with a two-week AI Readiness Sprint.
Fixed scope, fixed price, and a clear recommendation at the end, whether or not you continue with us.