Systems that reason,
decide, and act.
Agentic software that plans multi-step work, calls your tools, and closes the loop. Not a text box that sometimes guesses right — an autonomous operator embedded in your business.
Responds to questions. Holds a conversation. Waits for the next prompt.
- —Single-turn Q&A
- —Static knowledge
- —No side effects
Plans, decides, executes. Reads your systems, writes to them, closes the loop.
- Multi-step reasoning
- Live tool & data access
- End-to-end execution
A tool that responds
versus a system that operates.
Most AI deployments stop at a chatbot. That's not what we build. We build agentic systems — software that reasons through multi-step problems, makes decisions against real data, and executes workflows with minimal human involvement.
An agent might receive a customer inquiry, classify the intent, pull order history from your database, check shipment status via API, determine whether a refund or reshipment is appropriate, draft the response, and route it for approval if the dollar amount exceeds a threshold — all in seconds.
We handle the full engineering scope: LLM selection, prompt architecture, tool integration, memory and context, guardrails, observability, and human-in-the-loop escalation. Production systems with real reliability.
Seven layers,
one production system.
Multi-Step AI Agents
Autonomous agents that decompose complex objectives into executable steps, select the right tools for each step, and self-correct when something goes wrong. Reasoning systems that handle ambiguity, adapt to edge cases, and escalate when they should.
Tool-Calling & System Integration
The integration layer that connects agents to internal APIs, databases, SaaS platforms, and third-party services. Strict input validation, retry logic, and full audit trails on every tool call. Safe writes, not just reads.
RAG Pipelines & Knowledge Systems
Retrieval-augmented generation grounded in your actual data. Ingestion pipelines, chunking strategies, embedding selection, hybrid search, reranking, and citation tracking — engineered for your specific content and tested rigorously.
Prompt Architecture & LLM Selection
Structured prompt design with system prompts, few-shot examples, chain-of-thought scaffolding, and output schemas that produce consistent behavior across thousands of executions. Model selection driven by your requirements, not hype.
Memory & Context Management
Short-term conversation context, long-term user preferences, and episodic recall of prior interactions. Retrieval and summarization strategies that keep agents grounded without blowing through token limits or degrading reasoning quality.
Guardrails, Safety & Human-in-the-Loop
Escalation logic routes high-stakes or ambiguous situations to a human reviewer with full context attached. Output validation, prompt-injection defenses, PII detection, and compliance constraints enforced at the system level.
Observability & Evaluation
Structured logging of every LLM call, tool invocation, and decision. Token usage, latency, cost, retrieval quality, and error rates tracked in real time. Evaluation frameworks that regression-test your agent before every release.
Real workflows,
real outcomes.
If a workflow is repetitive, multi-step, data-dependent, and currently requires a human to coordinate, it's a candidate for an agent. These are the patterns we see most often.
Operations & Back Office
Purchase order processing, invoice matching, vendor communication, and approval routing. Agents that pull data from email, cross-reference systems, generate documents, and route them for sign-off.
Customer Support & Service
Agents that resolve tickets end-to-end by reaching order data, account history, and policy rules. Escalation on negative sentiment, complex issues, or high dollar value. Multi-channel deployment across chat, email, and voice.
Knowledge Management
Conversational interfaces over internal documentation, SOPs, engineering specs, and institutional knowledge. Agents that help employees find answers and surface historical decisions — instead of searching SharePoint folders.
Sales & Revenue Operations
Lead qualification against your ICP, third-party enrichment, personalized outreach drafts, and routing to the right rep. Pipeline agents that flag stalled deals, suggest next actions, and auto-generate follow-ups.
Document Intelligence
Extraction, classification, and routing of unstructured documents — contracts, invoices, compliance filings, inspection reports. Agents that read documents the way a human analyst would and push structured data into systems of record.
Industrial & Manufacturing
Agents connected to historians and SCADA systems that monitor production data, flag anomalies, generate shift reports, and answer natural-language queries about plant performance.
From workflow map
to live system.
Define the workflow.
Before writing code, we map the exact workflow — every decision point, system it touches, edge case, and escalation condition. This step usually reveals that the real complexity isn't the AI, it's the business logic nobody has documented.
Design the architecture.
Agent framework, LLM selection, tool schemas, memory strategy, guardrails. Single-agent vs. multi-agent. Synchronous vs. async. How the system interfaces with your existing infrastructure.
Build and integrate.
Tool connectors, RAG pipelines, prompt engineering, output parsers, orchestration. We build against your real data and systems from day one — not mock APIs and sample documents.
Evaluate and harden.
Systematic testing against labeled datasets, adversarial inputs, and edge cases. We measure accuracy, latency, cost, and failure modes. The agent doesn't ship until it meets quantitative thresholds on your actual workload.
Deploy and monitor.
Production deployment with observability, alerting, and logging. Dashboards that track performance in real time and evaluation pipelines that catch degradation early. Post-launch, we tune prompts, adjust tool logic, and retrain retrieval.
The stack we reach for.
Tools earn their place by being the right fit for the system, not by being fashionable. We select per-project based on requirements, cost, latency, and your existing infrastructure.
Engineering posture,
not marketing posture.
We build to ship, not showcase.
The gap between a working demo and a production agent is enormous. Demos handle the happy path. Production handles the 15% of inputs that are ambiguous, malformed, contradictory, or adversarial. We engineer for the ugly cases from the start because that's where agent projects actually fail.
We own the full stack.
LLM layer, orchestration, tool integrations, data infrastructure, deployment, and monitoring. No subcontractors, no "we'll handle the AI part and you figure out the integration." One team, one architecture, one accountable partner.
We measure everything.
Every agent ships with quantitative evaluation: accuracy on your test set, latency percentiles, cost per execution, failure rate, and escalation rate. You get a performance baseline on day one and a framework for tracking how it evolves.
We integrate with what you have.
We don't ask you to rip out your CRM, migrate to a new database, or adopt a new project management tool. Agents connect to your existing systems through clean integration layers. Your team keeps working the way they work.
Straight answers.
Simple single-agent workflows (document processing, FAQ, lead routing) typically take 4–6 weeks from kickoff to production. Complex multi-agent systems with deep integrations and custom evaluation frameworks take 8–16 weeks. The biggest variable is usually integration complexity with your existing systems, not the AI layer itself.
It depends on volume, model selection, and task complexity. A support agent handling 500 conversations per day on Claude Haiku might cost $50–100/month in LLM fees. A complex multi-step agent using GPT-4o on every call at high volume could be $2,000+/month. We optimize for cost during architecture design — using cheaper models for simple subtasks and reserving expensive models for reasoning-heavy steps.
Yes. We deploy agents in environments with PII, PHI, financial data, and compliance requirements. Architecture choices (self-hosted models, data residency, encryption, audit logging, role-based access) are driven by your regulatory context. We've built systems that meet SOC 2, HIPAA, and internal security review requirements.
It will, eventually. The question is whether the system is designed to catch errors before they reach the end user. Guardrails, confidence thresholds, human-in-the-loop routing, and automated evaluation exist specifically for this. We also build feedback loops so the system improves from corrections over time rather than repeating the same mistakes.
For RAG-based agents, you provide the knowledge base (documents, SOPs, FAQs, etc.) and we handle ingestion and retrieval engineering. For task-specific fine-tuning (rare — most projects don't need it), we work with you to curate labeled examples. For most agent projects, your existing documents and system access are sufficient to get started.
Build an agent that works.
If you have a workflow that's repetitive, multi-step, and data-dependent — tell us about it and we'll scope the system.