We build AI-driven automation for the repetitive work your team is currently doing by hand: triage, classification, extraction, drafting, routing. Connected to your stack, monitored, and reversible.
Trusted by teams shipping production software
We build deterministic pipelines with LLM decision points: trigger → AI judgment step → action → output. Inputs and outputs are structured, edge cases route to humans with full context, and every run is logged and reversible. Cheaper than agents, easier to monitor, and the failure mode is “a human reviews it.”
Your team does AI-able work by hand.
Support triages hundreds of tickets a day. Ops classifies expenses one by one. Finance retypes PDF invoices into the ERP. None of it needs senior judgment — just reading, deciding, and writing something predictable.
Zapier breaks where judgment starts.
If-this-then-that plumbing moves data fine. But the moment a step needs to decide which lead is qualified or which ticket is urgent, the no-code tools stall and the work lands back on a person.
The work lives across a dozen tools.
The trigger is in your CRM, the data in your warehouse, the action in your helpdesk. Without something stitching it together, every handoff is a manual copy-paste.
The repeating shapes that come up across most businesses. Yours probably has 2–3 of these — and that's where we start.
- 01
Lead qualification + routing
Inbound leads classified by fit, enriched from public data, routed to the right rep — automatically.
- 02
Support ticket triage
Classify by category, sentiment, urgency. Draft initial replies. Escalate the right ones to humans with context.
- 03
Document extraction
Invoices, contracts, forms, receipts — extract structured data into your ERP, CRM, or accounting system.
- 04
Content classification
Tag UGC, moderate inputs, categorize support history, enrich product catalogs. The deeply boring work that AI is great at.
- 05
Draft generation
Proposals, follow-ups, status updates, meeting recaps. A human reviews and sends — drafting time goes from hours to minutes.
- 06
Meeting → action items
Transcripts in, structured action items + owner + due date out. Wired into your project tool of choice.
- 07
Expense classification
Bank/credit-card transactions classified to your chart of accounts. Outliers flagged for human review.
- 08
Status report generation
Pull from project tools, ERP, CRM. Generate weekly status reports per client or per project. Reviewed before send.
- 09
Content moderation
Filter UGC against your policy. Flag edge cases. Tunable thresholds. Auditable decisions.
We start by measuring what the manual version actually costs, then build against real historical data — not a happy-path demo.
Map the workflow
1 week. Real workflow, real data, what 'good' looks like. We measure baseline cost — humans × hours × volume — so the ROI is grounded before we build anything.
Design the pipeline
1 week. Trigger, steps, AI decision points, error paths, human-in-the-loop. Schemas locked so inputs and outputs are predictable, not creative.
Build + test
2–3 weeks. Pipeline built, integrations wired, eval suite run against real historical data. Quality is measured, not assumed.
Pilot
1–2 weeks. Runs on real traffic with human review on every output. We tune thresholds until the 80% case is reliable.
Run
Production deploy, monitoring, ongoing tuning. Bad outputs are flagged automatically and routed back to a human.
Deterministic-first, model-agnostic. We reach for the cheapest tool that does the job and only add LLM judgment where a human would have to think.
- Claude Haiku / SonnetDecision model
- GPT-4o-miniDecision model
- EMBEmbeddingsClassification + search
- LangChainPipeline framework
- LangGraphPipeline framework
- INInngestDurable workflows
- TETemporalDurable workflows
- WFWorkflow enginesStep runner
- REST / GraphQLAPI layer
- WHWebhooksEvent triggers
- ZAPZapier / n8nWhere useful
- {}Custom connectorsBespoke glue
- PostgresDatabase
- S3 / R2Object storage
- pgvectorVector store
- LangSmithTracing
- HeHeliconeLLM analytics
- SeSentryError tracking
- {}Custom logsLogging
- VercelHosting
- AWSAWS LambdaServerless
- Cloudflare WorkersEdge runtime
- ModalGPU compute
“Futur Labs shipped in six weeks what our internal team couldn't in eighteen months.”
Focused automation
One workflow automated end-to-end. The fastest payback shape — pick the task your team does most by hand.
- 1 workflow
- Up to 5 integrations
- Eval suite + monitoring
- 3–5 weeks to ship
Agency ERP
Automated pipelines inside our own ERP: client triage, scope review, and weekly status reports generated from project data and reviewed before send. The same deterministic-pipeline pattern we ship for clients.
Arlo
Our own product. An MCP connector that lets Claude query 100+ analytics platforms in natural language — turning a manual reporting workflow into one structured request. Pass-through architecture; never stores client data.
Agents are autonomous: they reason, plan, and pick actions. Workflow automation is deterministic: a clear input goes through known steps to a known output, with AI used for the parts that require judgment (classify, summarize, extract, draft). Most teams need workflow automation first — it's cheaper, easier to debug, and easier to monitor than agents.
Zapier and n8n are great for moving data between SaaS tools when the logic is simple (if-this-then-that). Where they break down: anything that needs judgment (which lead is qualified, which support ticket is urgent, what should the email say). That's where AI workflow automation shines — Zapier-style plumbing with LLM-powered decision points wired in.
Lead qualification and enrichment, support ticket triage and routing, content classification, document extraction (invoices, contracts, forms), draft generation (proposals, follow-ups, summaries), meeting transcripts to action items, status report generation, expense classification, content moderation. If a human is doing the same kind of work over and over, it's a candidate.
Usually no — it replaces the boring parts of their jobs. The pattern that works: AI does the 80% case automatically, humans handle the 20% edge cases (with full context provided by the system). Your team moves up the value chain.
Workflow automation is generally cheaper than agentic systems because runs are deterministic and shorter. Model costs typically $50–$500/month depending on volume. We model your specific case in discovery.
Yes. We build automation that connects to Salesforce, HubSpot, Zendesk, Intercom, Jira, Slack, Gmail, Notion, your data warehouse, custom APIs, file storage. If there's an API or a webhook, we can wire it in.
A few questions about the project so we come prepared — then we'll set up a short call to dig in.