Eliminate repetitive work with AI
Every business runs on repetitive processes — lead routing, invoice handling, email triage, report generation. We build custom AI-powered workflows that run these processes automatically, giving your team back 10–20 hours per week and removing the errors that come from manual work.
Workflow automation is no longer just about connecting two apps with a webhook. Modern AI-powered automations use large language models to extract data from unstructured documents, classify and route messages, draft responses in brand voice, and make judgement calls that previously required human review. Deloitte reports that 78% of organisations are actively scaling AI, with the biggest ROI coming from process automation rather than customer-facing AI. McKinsey estimates that 60–70% of the hours worked in many knowledge-work roles could be augmented or automated with current generative AI capabilities. The result is faster cycle times, lower operating costs, and employees freed to do work that actually requires their expertise.
78%
of organisations are actively scaling AI across their operations
60–70%
of knowledge-work hours could be augmented or automated with generative AI
$1.4T
projected workflow automation market size by 2029
Core capabilities
Sales & lead automation
Lead scoring, CRM enrichment, form-to-meeting scheduling, follow-up sequences, and AI-drafted personalised outreach at scale.
Email & inbox automation
Triage inboxes, auto-categorise, draft responses in your voice, extract action items, and push structured data to downstream tools.
Document intelligence
Extract data from invoices, contracts, receipts, resumes, and forms using vision-capable LLMs — 95%+ accuracy, no template per document type.
Content operations
Governed content pipelines — research briefs, first drafts, editorial review, SEO optimisation, and publishing across channels.
Reporting & analytics automation
Daily/weekly report generation, cross-tool data aggregation, anomaly detection, and AI-written executive summaries.
Operations & back-office
Invoice processing, supplier onboarding, expense approval, inventory updates, and finance reconciliation workflows.
The automation stack we build on
We build on proven platforms, matched to your technical comfort and budget. For visual, low-code automations we use leading no-code platforms — fast to ship, great for teams that want to maintain the flows themselves. For more complex logic or self-hosted requirements we use open-source automation platforms, which give full code execution, custom nodes, and no per-task fees. For mission-critical, high-volume workflows we build custom Node.js or Python services deployed to your cloud of choice. For the AI layer we use leading frontier and open-source models via API, with fallbacks and retries built in. Every automation ships with logging, error alerts, and a run dashboard so you can see exactly what ran, when, and why.
How to identify your best automation opportunities
- High frequency + low complexity: anything your team does daily with roughly the same steps is a candidate — lead routing, email triage, data entry.
- Clear input and output: tasks with defined inputs (an email, a form, a document) and predictable outputs automate cleanly.
- Rule-based or pattern-based judgement: classification, extraction, and decisioning work that follows learnable patterns.
- High cost of delay: processes where humans currently slow things down (after-hours leads, overnight support, timezone gaps).
- Error-prone manual work: anything where typos, missed fields, or copy-paste errors cost money — invoicing, data sync, compliance reporting.
Why AI transforms traditional automation
Classic workflow automation handled structured data moving between two APIs. AI-powered automation handles the messy 80% of business data that is unstructured — emails, PDFs, call transcripts, handwritten notes, chat threads, customer feedback. An LLM step in your workflow can read an email and decide whether it needs urgent attention, extract the five relevant fields from a vendor invoice with no fixed template, classify a support ticket against 47 categories, or rewrite a meeting note into a structured summary with action items. This is the step-change that makes 2026 automations capable of replacing work that seemed "too human" for 2020 automation.
Starting small vs. big-bang automation
We always recommend starting with one high-volume, high-friction workflow rather than trying to automate an entire function at once. HBR's research on digital transformation shows that 70% of large-scale transformation efforts fall short of their goals, often because scope outpaces organisational readiness. A single automation that saves 5 hours per week and is adopted 100% by the team is worth more than a sprawling system that nobody trusts. Once the first win is measured and operating reliably for 30 days, we expand.
Real-world use cases
Recruitment agency
Automated resume screening and candidate pre-qualification using AI-based extraction and scoring.
Outcome: Cut screening time from 15 minutes per CV to 30 seconds, processed 10x more applicants per month with the same recruitment team.
Accounting firm
Invoice and receipt data extraction with auto-coding against the chart of accounts and sync to their accounting platform.
Outcome: Reduced bookkeeping data-entry time by 82%, eliminated manual transcription errors, faster month-end close.
B2B SaaS
Inbound lead enrichment: new signups enriched with company data, scored, routed to SDRs, and booked into calendars automatically.
Outcome: Meeting booking rate doubled, SDRs now focus on calls not research, attribution reporting fully automated.
Real estate
Automated property listing generation: agent uploads photos and notes, workflow drafts descriptions, selects best photos, posts to portals.
Outcome: Listing time dropped from 45 minutes to 4 minutes, agents listed 3x more properties per week.
Our delivery process
- 1
Process audit (free)
We run a 60-minute workshop to map your team's workflows, identify the top 5 automation opportunities, and score each by impact, effort, and ROI.
- 2
Design & prototype (1–2 weeks)
We document the target workflow, identify integration points, build a working prototype, and validate against 20–50 real examples.
- 3
Build & integrate (2–4 weeks)
We build the production workflow with error handling, logging, retries, and human-in-the-loop for edge cases. We integrate with your existing CRM, ERP, helpdesk, and comms tools.
- 4
Rollout & training (1 week)
We deploy to a pilot group, train the team on the run dashboard and exception handling, document the runbook, and hand over ownership.
- 5
Monitor & optimise (ongoing)
We review performance weekly for the first 30 days, tune prompts and rules based on real data, and identify the next workflow to automate.
What you get
Frequently asked questions
Which automation platform will you build on?
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It depends on your use case, team skill, and long-term total cost. Hosted no-code platforms are fastest to ship and have the broadest pre-built integrations — best for simpler workflows and non-technical teams that want to maintain flows themselves. Self-hostable open-source platforms are best when you need custom logic, data privacy, or no per-task fees. For mission-critical, high-volume workflows we build custom Node.js or Python services. We recommend the right fit after the audit — not before.
What about enterprise platforms or fully custom code?
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Enterprise automation platforms are a strong choice if you are already standardised on a matching productivity suite — we build on them when it makes sense. Custom services make sense for high-volume, business-critical workflows where the cost of platform fees exceeds engineering time. We recommend the simplest tool that meets your reliability and scale requirements.
How much does a typical automation cost?
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Simple workflows (single trigger, 3–5 steps, one AI call): $1,500–4,000. Medium complexity (multi-step, multiple integrations, document intelligence): $5,000–15,000. Complex multi-workflow systems with custom UI: $20,000–60,000. Monthly running costs depend on platform and AI usage — typically $20–500/month for small-to-medium businesses.
How do you handle data privacy?
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Data privacy drives architecture choice. For sensitive data (health, legal, financial, PII-heavy), we self-host on open-source platforms or build custom services in your cloud, use enterprise AI APIs with zero-retention agreements, and never log prompts or outputs that contain sensitive data. For non-sensitive workflows, standard SaaS platforms are fine.
What if the AI makes a mistake?
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Every workflow we build includes a confidence threshold — low-confidence outputs route to a human review queue rather than auto-executing. We also build retry logic for transient errors, fallback to simpler logic when the AI fails, and full audit logs so any issue can be traced and fixed.
Will this replace my team?
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No. Our goal is to remove the 30–40% of work your team hates — copy-paste, data entry, repetitive writing — so they can focus on relationships, strategy, and judgement. Clients typically redeploy the time saved into growth work (more clients, deeper service, better quality) rather than headcount reduction.