AI Automation Sprints, shipped fortnightly.

#What a sprint actually is

Two weeks. One automation. Production-grade. That's the shape.

The vast majority of "AI consulting" engagements stall because the scope is "help us figure out how to use AI." We sell sprints because the answer is always the same: pick one painful, recurring task, automate it end-to-end, prove the savings, then move to the next one. Stack incremental wins. Don't try to boil the operations ocean in a roadmap doc.

If you have a candidate task in mind, the 20-min discovery call confirms whether a sprint is the right fit. If you don't, the call is a working session where we walk through your team's repeating work and pick the highest-leverage one together.

#The fortnight rhythm

#Week 1. Scope, prototype, instrument

• Day 1: Discovery call confirms the task. Sketch the input → decision → output graph on a whiteboard.
• Day 2: Stack decision (n8n vs LangChain vs custom). Repo, env, secrets, observability set up.
• Day 3–5: Working prototype against a sample dataset. Real data, real decisions, real outputs. Usually 60% accurate, which is fine for week 1.

By end of Friday, you can run the automation manually against ten test cases and see the result.

#Week 2. Productionize, monitor, hand off

• Day 6–7: Tighten the decision logic. Add the human-in-the-loop pattern where it's needed (escalation, approval, ambiguous-case routing).
• Day 8: Wire monitoring and alerting. Token budget. Kill-switch. Slack/email notifications for the cases that need a human.
• Day 9: Production deploy. The automation runs on real traffic.
• Day 10: Handoff. Runbook, cost report, edit guide. Friday demo on real production data.

By the end of week 2, the automation is running, monitored, owned, and saving you the time it was scoped to save.

#How we pick the right LLM stack

Sprint kickoffs always include a 30-min "pick the stack" decision. There are three default shapes:

#Shape 1. N8n flow with LLM nodes

For most automations, this is the right answer. n8n gives you a visual editor your ops team can read, code nodes for the parts that need real logic, native integrations with the SaaS tools you already use, and self-hosted ownership. We use n8n for ~60% of sprints.

When it fits: the automation is mostly orchestration with one or two LLM decision points, integrates with a handful of SaaS tools, and is OK to maintain visually.

#Shape 2. LangChain / LangGraph in TypeScript

When the automation has multi-step reasoning, agentic patterns, or non-trivial state machines, LangGraph gives us first-class support for tool use, retries, and observability. It's more code than n8n but more flexible at the edges.

When it fits: the automation is "agent-like". It makes a sequence of decisions, uses multiple tools, and may need to revise or back-track.

#Shape 3. Custom Node service

When the automation is hot-path (called many times per second), latency-sensitive, or needs to run inside your existing application infrastructure, we ship a plain Node service. The LLM provider sits behind a small interface so we can swap providers in a config file.

When it fits: the automation runs at scale, is part of your product surface (not internal ops), or has hard latency requirements.

We pick the shape on the call. The price is the same regardless. You're not paying us for code-lines, you're paying us for the automation.

#What "production-grade" means

Every sprint ships with the same operational baseline. Skipping any of these is what turns a clever demo into a 3am incident the team can't debug.

#Single Sprint vs Triple Sprint. Pick

Single Sprint ($4,800, 2 weeks): one automation, fully shipped. The right call when you have one painful task and want to validate the sprint approach before scaling up.

Triple Sprint ($12,800, 6 weeks): three sequenced automations plus the cross-cutting monitoring and integrations. The right call when you have a clear set of three tasks (most ops teams do) and want to amortize the setup cost. Per-automation cost drops by ~30% versus three separate Single Sprints.

After the first sprint, most teams move to a custom retainer for ongoing automation work. Usually 1–2 new automations per month plus tuning. We treat retainers as quote-on-scope, starting at $5,000/month with a 3-month minimum.

Not sure which tier fits your scope? Try the AI automation ROI calculator — enter your hours-saved estimate, hourly rate, team size, and complexity, and see year-1 value, payback in weeks, and 12-month ROI across all four engagement tiers.

#A concrete example

The most common sprint we ship for B2B SaaS teams: inbound lead enrichment + scoring.

The task: every form-fill creates a row in your CRM. A human SDR reviews it, looks up the company on LinkedIn, scores the fit, assigns to an AE, drafts a personalized first-touch email. Average time per lead: 8–12 minutes. Average leads/day: 30. Total: 4–6 hours of SDR time per day spent on enrichment, not on conversations.

What we ship in 2 weeks:

• Webhook listener picks up new form-fills from your existing form (Typeform, HubSpot form, custom)
• Enrichment chain pulls company data (Clearbit / Apollo / Companies House) and a recent news scan
• Scoring LLM rates fit against your ICP (criteria you define on day 1)
• High-fit leads route to AE Slack with a draft personalized email; medium-fit to SDR queue; low-fit auto-replied
• Every decision is logged with reasoning so the team can audit and tune

Median customer report after 30 days: SDR time on enrichment drops 70–80%; conversion on AE-routed leads improves because the personalization is consistent and well-researched.

That's one sprint. Then the next sprint is content QA, or invoice extraction, or daily exec report generation. Whatever the next painful task is.

#When AI automation is the wrong tool

We turn down sprints in a few specific cases:

• The task is genuinely ambiguous and high-stakes. Legal review of a contract, medical triage, employee performance review. Those benefit from AI augmentation but not full automation. We'll suggest scoping it as an assistant tool, not an automation.
• The task happens once a week and takes 15 minutes. The math doesn't work. You spend more on the build than the automation saves you in 5 years.
• The data is genuinely sensitive and the team doesn't want it touching an LLM. We can ship local-model versions, but the savings vs. a hosted model are usually narrower than the team imagines. We'll be honest about the tradeoff on the call.
• The team isn't ready to own the automation. If nobody on your side will tune the prompts or read the monitoring after we leave, the automation will rot. We'll suggest starting with one team member on a tweak retainer.

#Ready when you are

The 20-min discovery call confirms whether a sprint is the right fit and which automation to start with. We come back with a fixed-price proposal in 48 hours.