Automation & Data

Most enterprises waste 20-40% of their operational headcount on manual data entry, copy-paste between systems, and reporting busywork. Automation done well frees that capacity for the work humans are actually paid to do.

Our automation practice covers three layers: workflow automation (the visual platforms), data pipelines (ETL and warehouse), and reporting automation (dashboards and scheduled exports). We work across all three because they are usually entangled — a “workflow problem” is often actually a “data quality problem” upstream.

Where automation moves the needle

The patterns we see consistently delivering ROI in 2026:

• Inbound triage — lead routing, support ticket classification, document intake. LLM-augmented decisions plus rule-based execution.
• Outbound orchestration — sales prospecting, multi-channel nurture flows, partner outreach. Personalisation at scale via LLM drafting + human review.
• Reporting — eliminating the weekly “pull the numbers” task by piping data into a single dashboard updated on a schedule.
• Document processing — invoices, contracts, KYC packets. Vision-capable LLMs plus a thin rule layer; 95%+ accuracy with proper eval discipline.
• Cross-system synchronisation — keeping CRM, billing system, and product database in agreement. Webhook-driven workflows replacing brittle cron-based sync.

What we do not recommend: full agentic autonomy on customer-facing channels. The 5-10% of cases an AI agent gets wrong are exactly the cases that destroy trust. Keep humans in the loop on outbound communications.

What we deliver

Discovery (week 1)

• Process audit: which workflows are bottlenecks, which are theatre, which are genuinely automatable
• Tool landscape review: what platforms you already use, what they cost, what they could do but do not
• Quick wins ranked by ROI (time saved per month / build effort per month)

Build (weeks 2-8)

• 5-15 production workflows depending on engagement size
• Documentation for each: trigger conditions, data flow, failure handling, rollback procedure
• Observability layer: which workflows ran today, which failed, what data they processed
• Training for your team on maintenance and small changes

Data pipeline work (where in scope)

• ELT pipeline from operational systems (CRM, billing, product DB) to a warehouse
• dbt models for the canonical metrics your team reports on
• Dashboards in Looker Studio, Metabase, or Superset
• Quality alerts (volume anomalies, dimension cardinality drift) on the canonical models

Handover or ongoing partnership

• Documentation of every workflow as living markdown in your repo
• Quarterly review of which workflows are still useful, which need updates, which can retire

A typical mid-engagement workflow inventory

Sample of what we delivered for a mid-market B2B SaaS client (anonymised):

Aggregate: ~30 hours/week saved across a 6-person team. Tooling cost: ~USD 1,400/month (self-hosted n8n + Claude API + enrichment). Payback in week 3.

Engagement shapes and pricing anchors

For directional planning, three engagement archetypes:

Automation audit (2-3 weeks)

• Process audit identifying the 10-15 most automatable workflows in your business
• Tool landscape review with cost / capability mapping
• ROI ranking: which automations pay back in months 1-3 vs months 6-12
• Recommended platform stack (n8n vs Zapier vs Make decision)
• Typical investment: USD 12-25K depending on operational complexity

Build engagement (8-16 weeks)

• 5-15 production workflows shipped, depending on engagement size
• n8n self-hosted infrastructure stood up (or your platform of choice)
• Observability + error-handling + rollback procedures documented
• Optional data pipeline work (Airbyte / Fivetran ELT + dbt + warehouse)
• Training and handover to your in-house team
• Typical investment: USD 60-150K depending on workflow count and pipeline complexity

Operations partnership (6-12 months)

• Quarterly review of workflow performance
• New workflow design as your business evolves
• Platform monitoring and maintenance support
• Typical investment: USD 5-12K/month depending on workflow volume

These are anchors, not quotes. Every proposal tailors to actual scope.

When you should NOT engage us

Honest about when we are not the right fit:

• Hoping automation will fix a broken process — automating a broken process produces broken results faster. We will tell you to fix the process first; if you do not want to, we are not the right firm
• Wanting full lights-out automation immediately — production-grade automation requires human-in-the-loop for the cases the system gets wrong. Aspirations of “fully autonomous” agentic systems for customer-facing channels are not realistic in 2026; we will not pretend they are
• Looking for the cheapest platform regardless of fit — we recommend platforms that match workload economics, not platforms that minimise license cost. If platform cost dominates the decision, we are probably not the firm to engage
• No internal owner for the automation function — automation systems are software; software requires ownership. Without an internal owner, workflows erode within 6 months of go-live

Get in touch

Email contact@kalastor.net with the 3-5 manual processes your team finds most painful. We respond within 24 hours and propose a scoping call.

Adjacent reading: Our anonymised automation ROI case study and n8n vs Zapier vs Make comparison.