Home/Services/AI Automation
Service · 01

AI agents that quietly run the parts of your business you used to hire for.

Not chatbots that read your FAQ back to you. Real agentic systems — planning, using tools, taking action, learning from feedback — running against production data with humans in the loop only where it matters.

Talk to an engineer See deployments
42m
avg. ticket resolution
4.2×
throughput lift
3wk
to first production agent
98.7%
uptime SLA
What we build

Five kinds of AI systems.
All of them earn their keep.

01 · Flagship

AI Workflow Automation

End-to-end workflows where a chain of agents plans, calls tools, and hands off to humans only for the calls that need judgement. Typical wins: invoice triage, contract review, KYC prep, support routing.

  • LangGraph
  • Temporal
  • Human-in-loop
02

AI Agents

Task-specific agents with tools, memory, and observability. Not demos — production.

03

Chatbots & Copilots

Support, sales, and internal copilots grounded in your data with clean RAG and citations.

04

Business Process Automation

The unglamorous stuff — form intake, data reconciliation, cross-system sync — automated with the AI where it earns its cost, and plain code everywhere else. We don't put a model in the loop just to say we did.

  • OCR
  • ETL
  • Zapier
  • n8n
05

Integrations

Salesforce, HubSpot, Slack, Notion, Zendesk, SAP — clean two-way sync with retries, dead-letter queues, and audit logs. Nothing about your integrations should surprise you at 2am.

  • Salesforce
  • HubSpot
  • Slack
  • SAP
Benefits

What changes when the agent is doing it.

Faster by an order of magnitude

Tasks that took hours run in minutes — with an audit trail so you can prove it later.

Consistency you can measure

Same policy applied every time — with tests that catch regressions before your users do.

Human effort, redirected

Your team spends its hours on the calls that need judgement, not the ones that need copy-paste.

Technologies

The tools we actually use.

No mystery frameworks. Everything here is documented, hire-able, and battle-tested.

Foundation models

OpenAI GPT-4.xAnthropic ClaudeGeminiLlama 3Mistral

Agent frameworks

LangGraphLangChainInstructorDSPy

Vector & RAG

PineconeWeaviatepgvectorQdrant

Orchestration

Temporaln8nZapierPrefect

Observability

LangSmithLangfuseHelicone

Deployment

AWS BedrockAzure OpenAIVercelModal
FAQ

Common questions about AI automation.

How do you decide when to use an LLM vs. plain code?
If the task can be expressed as clear rules or a schema, we write plain code — it's cheaper, faster, and easier to test. LLMs come in where the input is ambiguous, unstructured, or requires judgement. Most working systems we build are 70% deterministic code and 30% model.
What about hallucinations and safety?
Every production agent we ship has bounded tool access, structured outputs where possible, evaluations that run in CI, and human review gates for high-stakes decisions. We don't ship agents that can take irreversible actions without oversight.
Do we need to send our data to OpenAI?
No. We deploy on AWS Bedrock, Azure OpenAI, or self-hosted open-weight models depending on your compliance needs. Data never leaves your cloud if you don't want it to.
How do you measure whether an agent is working?
Two layers. Business metrics (throughput, resolution time, deflection rate) reported weekly. Model evals (accuracy, groundedness, tool-use correctness) running on every deployment. Both go to a dashboard you own.
What if the model gets deprecated?
We build model-agnostic. Every system uses an abstraction over the provider, and our evals let you swap models without regressing.

Bring the workflow.
We'll bring the agents.

30-minute call with an ML engineer. If the automation makes sense, we'll say so. If it doesn't, we'll say that too.

Book consultation