AI agents that work in production. Deployed systems, not demos.
We design, build, and deploy autonomous AI agents for mid-market companies. Workflow design, multi-agent orchestration, data engineering, governance, and monitoring. Deployed systems, not demos.
The Problem
Agentic AI is overhyped and underdelivered.
Demos that collapse in production: An agent that handles fifty clean documents in a sandbox is a different animal from one that processes ten thousand production documents with inconsistent formatting, missing fields, and edge cases your vendor never considered. That gap between demo and deployed system is where most agentic AI projects die.
No one owns the unglamorous work: About 80% of what makes an agentic system work is invisible on a slide: data pipeline design, schema normalization, prompt alignment across failure modes, retry logic, access control, audit logging. Most AI vendors skip this work. We don't.
Multi-agent complexity nobody warned you about: Multiple specialized agents, each with its own context window, failure modes, and output format, create coordination problems that compound fast. Without careful architecture upfront, you get brittle chains that fail silently and are nearly impossible to debug.
Governance added as an afterthought: Compliance teams and risk officers aren't opposed to AI agents. They're opposed to agents they can't audit. When guardrails and logging get bolted on after the build, they hurt performance and delay deployment by months. Governance has to be a design input from day one.
No monitoring means no learning: An agentic system without monitoring degrades. Model providers change outputs, edge cases pile up, upstream data sources drift. Without observability built in from the start, you find out about problems from user complaints, not dashboards.
Our Approach
Four phases. One deployed system. We treat agentic AI implementation as an engineering problem with a business case attached. Not the other way around.
Phase 1 — Scoping & architecture design (Weeks 1-2): We map the target workflow end to end, identify where autonomous action makes sense, and define where human review is required. We assess your data infrastructure for agent readiness (source quality, access patterns, schema consistency) and produce an architecture document covering agent topology, orchestration, tool integrations, and governance. This phase regularly turns up mismatches between what a company wants and what their data supports. Better to find that in week one. Deliverable: Architecture specification document, data readiness gap list, governance framework outline, and revised scope
Phase 2 — Data engineering & pipeline build (Weeks 3-5): The unglamorous phase that determines whether the agent works at scale. We set up data pipelines, normalization logic, and retrieval infrastructure: chunking and indexing strategies for RAG, tool interfaces, API connectors, and logging for both monitoring and compliance. We also run adversarial data testing here, injecting malformed, missing, or ambiguous inputs to harden the system before agent development begins. Deliverable: Production-ready data pipelines, tool integrations, vector store or structured data layer, and adversarial test results
Phase 3 — Agent development & orchestration (Weeks 5-9): We assemble and test each agent against the architecture spec, then wire them together through the orchestration layer. For multi-agent systems: inter-agent communication protocols, handoff conditions, and fallback behaviors. Every agent handles the unhappy path explicitly: ambiguous inputs, tool failures, context limit violations, conflicting upstream signals. Human-in-the-loop checkpoints are first-class system components, not afterthoughts. Deliverable: Tested agent system with orchestration layer, human-in-the-loop checkpoints, and documented failure mode handling
Phase 4 — Deployment, monitoring & handoff (Weeks 9-11): We deploy to your environment and set up the observability stack: dashboards for performance, latency, error rates, and output quality, plus alerts for drift patterns that precede failures. A parallel operation period lets the agent handle live traffic alongside existing processes while we calibrate thresholds. Handoff includes technical documentation, an engineering runbook, and a monitoring playbook covering what to watch and when to act. Deliverable: Live deployed system, observability dashboards, technical documentation, runbook, and monitoring playbook
Deliverables
Architecture & Data (Weeks 1-5)
- Architecture specification covering agent topology, orchestration design, tool integrations, and governance framework
- Production data pipelines with normalization, validation, and adversarial testing
- Retrieval infrastructure, tool connectors, and audit logging layer
Agent Build & Orchestration (Weeks 5-9)
- Fully tested agentic system with multi-agent orchestration (where applicable)
- Human-in-the-loop checkpoint implementation per governance spec
- Documented failure mode handling and edge case coverage
Deployment & Operations (Weeks 9-11)
- Live deployment to your environment with parallel operation period
- Observability dashboards with performance, quality, and drift metrics
- Full technical documentation, engineering runbook, and monitoring playbook
Who This Is For
Right for you if: You have a specific workflow in mind (approval routing, document processing, research synthesis, customer escalation triage) and need a team that can build it end to end, including all the data work most vendors skip.. You've watched a vendor demo an AI agent that looked impressive and then fell apart on your data. You want an implementation partner who treats data engineering as the core of the work.. You operate in a regulated environment or have internal governance requirements and need auditable decision trails, human checkpoints, and documented failure handling from day one..
Not right if: You don't yet know which workflow you want to automate. Start with our AI Strategy & Diagnostic to identify and prioritize the right use case before committing to a build.. You're looking for a proof-of-concept or a prototype for an investor demo. We build systems that run in production. If your goal is a demo, we're not the right team.. Your data infrastructure isn't ready and you're not prepared to invest in fixing it. Agentic systems are only as reliable as the data they run on. We'll find gaps in the architecture phase, but we can't build on a broken foundation..
Use Cases
Financial Services: A B2B SaaS platform was processing loan applications manually across a team of twelve credit analysts. Each application required document collection, identity verification, income analysis, and a preliminary credit narrative before formal underwriting could begin. Average processing time was four hours per application. — Built a multi-agent document processing system with three specialized agents: one for document extraction and normalization, one for cross-referencing identity and financial data against external APIs, and one for generating structured preliminary credit narratives. The orchestration layer had explicit handoff conditions and a human review checkpoint before any output reached the underwriting queue. Data pipeline work consumed roughly two-thirds of the engagement.. Outcome: Preliminary processing time dropped from four hours to twenty-two minutes per application. Analyst capacity shifted to complex cases and final credit decisions. It handles about 85% of applications without manual intervention in the early stages.
Legal & Professional Services: A legal services firm was spending significant associate time on initial contract review: identifying non-standard clauses, flagging deviations from preferred positions, and summarizing key commercial terms before senior lawyer review. The work was consistent enough to automate but varied enough that rule-based systems had repeatedly failed. — Built an agentic contract review system that ingests uploaded contracts, classifies clause types against a firm-defined taxonomy, flags deviations from standard positions with confidence scores, and produces structured review summaries in the firm's internal format. Explicit handling for clause ambiguity and missing sections returns structured uncertainty flags rather than silent omissions. Compliance logging captures every classification decision for audit.. Outcome: Initial contract review time cut by about 70%. Associates now review agent-generated summaries and confirm flagged items rather than reading contracts from scratch. Senior lawyer review time unchanged. The agent compresses associate preparation time, not judgment time.
B2B SaaS / Operations: A SaaS company serving the logistics sector was managing customer support across a high volume of tickets with a small team. Tier-1 triage (routing, categorization, and resolution for known issues) consumed most of the support team's capacity and slowed response times for complex escalations. — Built an agentic triage and resolution system that classifies incoming tickets, resolves known issue types against a continuously updated knowledge base, and routes novel issues to the right specialist with a structured context summary. Integrated with their existing helpdesk platform. The escalation logic uses explicit confidence thresholds so the system defaults to human routing when it isn't certain rather than attempting resolution on edge cases.. Outcome: About 60% of incoming tickets handled end to end without human intervention. First-response time for escalated issues improved 65% because specialists receive pre-triaged, context-rich tickets. Support team shifted to customer success and expansion revenue.
Results
What a full agentic implementation looks like.
Financial services - mid-market lending platform: 85% of loan applications processed without manual early-stage intervention. A lending platform with 200+ employees came to us after a vendor had delivered a document extraction agent that performed well in demos and failed within a week of going live. The failure was predictable in retrospect: the agent had been built against clean sample documents and had no handling for inconsistent scan quality, mixed languages, and incomplete submissions that made up a significant fraction of actual applications. We restarted the architecture from the data layer. Three weeks of pipeline work (normalization logic, adversarial input testing, structured uncertainty handling) preceded any agent development. The final system used three coordinated agents with explicit handoff conditions and a human review checkpoint at the preliminary narrative stage. It went live after an eleven-day parallel operation period handling live traffic alongside the existing manual process. Preliminary processing time dropped from four hours to twenty-two minutes. It has run in production for seven months with no critical failures.
Frequently Asked Questions
How long does a typical agentic AI implementation take?
Ten to eleven weeks from kickoff to live deployment. Two weeks of architecture and scoping, three weeks of data engineering and pipeline build, four weeks of agent development and orchestration, two weeks of deployment with parallel operation. The data engineering phase is the one that most commonly expands when we find infrastructure gaps, which is why we assess data readiness in week one.
What makes this different from what most AI vendors offer?
Most vendors build agents. We build agents and everything they depend on: data pipelines, retrieval infrastructure, audit logging, monitoring, governance checkpoints. The demo-to-production gap that kills most agentic projects exists because vendors skip the infrastructure work. Our engagements cost more than a pure agent build because they include the work that determines whether the agent functions at scale.
Do I need to have my data infrastructure already in place?
No, but you need to be prepared to invest in it. We assess data readiness in week one and produce a gap list. If significant infrastructure work is needed (which it often is), we scope it into the engagement and build it ourselves. What we can't do is build a reliable agentic system on top of data that is fundamentally inaccessible or unstructured. If the gaps require a standalone data project first, we'll tell you before the build starts.
How do you handle governance and compliance requirements?
Governance is a design input, not a sign-off at the end. In the architecture phase, we define audit logging, human-in-the-loop checkpoints, and access control alongside the agent topology. For regulated industries, that means decision audit trails in the data layer, confidence thresholds below which the system routes to human review, and documented decision logic your compliance team can review. We've worked under RBI guidelines, HIPAA-adjacent requirements, and internal legal mandates.
What happens after the engagement ends? Who maintains the system?
You get a documented system: architecture docs, engineering runbook, and a monitoring playbook covering what to watch and what to do when performance degrades. Most clients maintain the system internally using the runbook. For teams without AI engineering capacity, we offer a retainer for ongoing monitoring, model updates, and system evolution. The retainer is separate and scoped based on system complexity.
Can you work with our existing tech stack and model providers?
Yes. We're model-agnostic and infrastructure-agnostic. We've built on OpenAI, Anthropic, Google, and open-source models, and deploy into AWS, GCP, Azure, and on-premise environments. The week-one architecture recommendation covers which model and infrastructure choices make sense for your use case, latency requirements, data residency, and cost profile. No vendor affiliations influencing those calls.
