Automate the work your team should never be doing by hand
We build production AI agents that survive real workloads, not slick demos that break on edge two. Multi-agent systems with observability, guardrails, and a human in the loop, built on LangGraph and CrewAI, live in 6 to 12 weeks.
Book an automation assessmentWe map which of your workflows are worth automating and
model the real hours and cost they take today.
25+
Product
200+
Engineers
5 Days
Embeds
Trusted by fintech teams across the US, UK, UAE, and Singapore
First, we find out if automation is even worth it for you
Before we build anything, we run an automation assessment. We look at the workflows your team repeats every day, measure the hours and cost they take now, and tell you which ones are worth automating and which are not. You leave with a short written map: the candidate workflows, the expected time saved, and a rough payback period. If nothing clears the bar, we tell you, and you have lost nothing but 30 minutes.
30 minutes. You keep the automation map either way.
Where agents earn their keep
Each workflow below comes with the signal that it is a good automation candidate. If a workflow matches the signal, it is worth a closer look.
Customer support triage
High volume, repetitive questions, clear knowledge base. Agents draft and resolve, humans handle the hard 20%.
Signal: high volume + clear knowledge baseSales and revenue ops
CRM hygiene, lead research, follow-up drafting, meeting notes turned into actions.
Signal: repetitive steps + structured systemsDocument and data extraction
Invoices, contracts, KYC packets, and forms turned into structured data.
Signal: high volume + consistent formatsInternal research
Pulling, comparing, and summarizing across many sources and tools.
Signal: many sources + senior time spentBack-office workflows
Multi-step processes that hop between systems and currently need a person to babysit them.
Signal: multi-step + cross-system handoffsThe pattern is always the same. High volume, rule-heavy, and currently eating senior people's time. If that describes a workflow, it is a candidate.
Anyone can demo an agent. Almost nobody ships one that survives.
A demo works once on a happy path. A production agent runs thousands of times against messy reality. The difference is everything we build that you never see in a demo.
works once on the happy path
Every agent run is traced, logged, and inspectable with Langfuse or LangSmith, so when something goes wrong you can see exactly where, not guess.
We test agents against real cases before and after every change, so an improvement cannot quietly break what already worked.
Input and output validation, PII redaction, and hard limits on what an agent is allowed to do or spend.
Confidence thresholds route the uncertain cases to a person instead of acting wrong with confidence.
Token and tool-call budgets, caching, and model routing, so the agent is cheaper than the work it replaces.
The demo is the easy 20%. The other 80% is why most agent projects quietly die in a slide deck. We build the 80%.
How an agent actually works
A production agent loops: it perceives the task, plans steps, calls tools and APIs through function calling and MCP, checks its own work, and either finishes or escalates. Memory carries context across steps, orchestration coordinates multiple agents when one is not enough. We design that loop for your workflow, then wire it to your real systems.
From assessment to live agents
Assessment
We map and cost your workflows and pick the highest-ROI candidates. Output: a written automation map.
Week 0Pilot
We build one agent or workflow end to end, with observability and evals from day one. Output: a working pilot on your real data.
Weeks 1 to 4Harden and expand
Guardrails, human-in-the-loop, and integration with your stack. We expand to the next workflows.
Weeks 4 to 10Run and improve
Monitoring, evals on every change, and tuning as your processes shift.
OngoingMost teams have a useful pilot live in 4 weeks and a hardened system in 6 to 12, depending on workflow count and integration complexity.
A sample of recent work
Scoped in 2 weeks. Shipped in parallel. Zero hours pulled from their core roadmap.
Embedded a dedicated team to ship an AI layer inside the client's existing stack. Built with evals, fallbacks, and monitoring from day one, integrated into their codebase and CI, documented and handed off clean.
From kickoff to clean report before their launch date.
Full audit of protocol contracts ahead of mainnet launch. Findings ranked by severity, fixes implemented and re-verified, final report delivered for investor and exchange due diligence.
References available under NDA. We link each engagement to a full case study once the client approves disclosure.
Read the case studies →Frequently asked questions
No. They take the repetitive 80% so your people do the judgment work. The human-in-the-loop design keeps a person on the cases that need one.
Guardrails, hard action and spend limits, confidence thresholds that escalate to a human, and evals that catch regressions before they ship.
LangGraph and CrewAI for orchestration, Langfuse or LangSmith for observability, and we choose the model per task across OpenAI, Anthropic Claude, Google Gemini, or open-weight models. We are not locked to one vendor.
Yes. They connect to your systems through APIs, function calling, and MCP. The agent works inside your stack, not beside it.
We design for your privacy requirements, including self-hosted or VPC deployment and no-training agreements with model providers. See the data section on generative AI development at /generative-ai-development.
You do. NDA on request.
Building something generative instead? See generative AI development.
Find out what a week of repetitive work
is actually costing you
Book a 30-minute automation assessment. We will map your repeatable workflows, model the hours and cost they take today, and tell you which ones are worth automating. No pitch. If automation will not pay for you, we will say so.
NDA on request. You keep the automation map either way.