AI agents that complete multi-step tasks
Agents that look up records, call your APIs and finish multi-step tasks inside defined permissions, with escalation rules for anything outside their remit.
AI Automation
AI automation that handles real work, not just demos
As an AI automation company, we build agents, LLM integrations and document pipelines that read, classify, draft and route work across your CRM, inbox and back-office systems. Every workflow ships with measurable targets, evaluation against your own data, and a clear path for people to review whatever the model is unsure about.
- Core capabilities
- 6
- Step delivery process
- 5
- Case studies
- 4
Overview
At a glance
AI agents and LLM workflows that handle the reading, sorting and drafting, then pass anything uncertain to your team.
AI automation applies large language models and related techniques to work that used to need a person to read, interpret or write: triaging support tickets, pulling fields out of invoices and contracts, drafting replies, summarizing calls, or deciding which queue a request belongs in. Done well, it removes hours of low-value handling from every week and shortens the gap between a request arriving and someone acting on it. Done poorly, it produces confident mistakes that someone has to clean up later.
Our AI automation work suits operations, finance, support and sales teams that handle a steady volume of text-heavy tasks and already know where the bottlenecks are. You don't need a data science department or a perfect data warehouse. You do need a process that repeats often enough to matter, a system of record we can connect to, and someone on your side who owns the outcome.
We start with discovery: mapping the current workflow, collecting real examples, and agreeing on targets such as handling time, straight-through rate or accuracy on a labeled sample. Then we choose the simplest approach that meets those targets, which is sometimes a well-structured prompt with retrieval and sometimes a multi-step agent with tool access. Every build includes an evaluation set drawn from your data, guardrails on what the system is allowed to do, confidence thresholds that send uncertain cases to a person, and logs you can audit. Sensitive data stays inside the boundaries you set, and we review each model provider's data-use terms with you before anything goes live.
A typical engagement runs in phases. A short discovery and prototype phase proves the approach on your own examples. The production build connects the workflow to your systems, adds monitoring and review screens, and rolls out to a portion of volume first. After launch we track quality and running costs, tune prompts and thresholds as new edge cases appear, and hand over documentation so your team knows exactly how the automation behaves.
Business problems solved
Where it pays off first
The operational bottlenecks AI removes for growing teams.
- Solved
Skilled people stuck doing copy-and-paste work
Analysts, coordinators and support staff spend hours re-keying data from emails, PDFs and portals into the systems that actually run the business.
- Solved
Requests waiting in shared inboxes to be sorted
Tickets, orders and inquiries sit until someone reads, categorizes and routes them, so response times depend on who happens to be online.
- Solved
AI pilots that never reach production
A chatbot demo impressed leadership, but nobody defined accuracy targets, error handling or ownership, so it stalled before it touched a real customer.
- Solved
No control over what a model says or does
Without evaluation, guardrails and audit logs, teams can't trust model output anywhere near customers, contracts or financial records.
Capabilities
What our AI work covers
Modular building blocks we combine into a solution that fits how your team already works.
LLM integrations and retrieval
We connect models from OpenAI, Anthropic and open-weight providers to your applications, grounded in your own content, with structured outputs and fallbacks when a provider is slow or unavailable.
Document processing and extraction
Invoices, purchase orders, contracts and application forms become validated, structured data. Low-confidence fields are flagged for review instead of guessed.
Workflow intelligence
Classification, prioritization and routing steps added to existing workflows, so each request reaches the right queue with a summary and a suggested next action attached.
Customer support automation
Assistants that answer common questions from your help center and order data, draft replies on complex tickets, and hand off to an agent with the full conversation history.
AI-assisted internal tools
Search, summarization and drafting tools built on your knowledge base, SOPs and CRM records, so staff find answers without digging through shared folders.
How we deliver
A clear path from idea to production
Every AI engagement follows the same transparent, milestone-driven process.
- 01
Workflow discovery
We map the current process, gather real examples and edge cases, and agree on the targets that define success, such as handling time or accuracy on a labeled sample.
- 02
Prototype on your data
A working prototype runs against a representative sample, so you see real output quality, failure modes and running costs before committing to a full build.
- 03
Production build and integration
We connect the automation to your systems of record, add guardrails, confidence thresholds and review screens, and log every decision for audit.
- 04
Controlled rollout
The workflow goes live on a portion of volume with people checking its output, then expands as it meets the agreed quality bar.
- 05
Monitor and improve
After launch we track accuracy, cost and exceptions, and retune prompts, retrieval and thresholds as your data and processes change.
Use cases
What teams build with it
Representative scenarios — every build is scoped to your data, systems and goals.
Use case 01
Invoice and PO matching
Line items on supplier invoices are extracted and matched against purchase orders and goods receipts, so accounts payable reviews only the mismatches instead of every document.
Use case 02
Support ticket triage and reply drafting
Incoming tickets are categorized, prioritized and routed with a drafted response, letting agents spend their time on conversations that need judgment.
Use case 03
Contract and application review
Key clauses, dates and missing information are pulled from contracts or application packs into a checklist, turning the first-pass review into a quick verification.
Use case 04
Sales call summaries into the CRM
Call transcripts become structured CRM notes with next steps, objections and follow-up tasks logged against the right deal, without reps typing them up.
Technology
Tools we build with
Proven, well-supported technology — chosen for your stack, not our preferences.
- Backend
- Node.js
- Python
- FastAPI
- Databases
- PostgreSQL
- Cloud
- AWS
- AI & ML
- OpenAI
- Anthropic Claude
- LangChain
FAQ
Questions about AI
How accurate will an AI automation be, and what happens when it gets something wrong?
Accuracy depends on the task, the quality of your examples and how much the inputs vary, so we measure it on a labeled sample of your own data before launch instead of quoting a generic figure. Every workflow has confidence thresholds: when the model is unsure or a validation check fails, the item goes to a person rather than being processed automatically. We log inputs, outputs and decisions, so errors can be traced, corrected and used to improve the system.
Is our data safe if you use models from providers like OpenAI or Anthropic?
We review each provider's data-use and retention terms with you before any build, and we only send a model the data a task actually needs. Depending on your requirements, we can redact personal fields before a model sees them, use cloud-hosted models in a region you choose, or run open-weight models on infrastructure you control. Access to prompts, logs and outputs is restricted by role, and we are glad to work under your NDA and data processing agreement.
Will AI automation work with the systems we already use?
In most cases, yes. We connect to CRMs, helpdesks, ERPs, email, document storage and databases through their APIs, webhooks or automation platforms such as n8n. If a system has no API, we look at exports, email-based intake or read-only database access before recommending anything more invasive. Your existing tools remain the system of record, and the automation reads from and writes to them.
What drives the cost of an AI automation project?
Cost depends more on scope and risk than on the AI itself. The main drivers are:
- Integrations: how many systems are involved and how well documented their APIs are
- Input variety: how many document layouts, languages or request types the system must handle
- Review requirements: the accuracy bar and how much human-in-the-loop tooling is needed
- Model usage: ongoing costs that scale with volume and the models chosen
We start with a short, fixed-scope discovery and prototype phase, so you have real quality and running-cost data before committing to a full build.
Start a project
Have a workflow that eats hours every week?
Send us the process and a handful of real examples. We'll tell you whether AI is the right fit, what accuracy is realistic, and what a first production release would involve.