Same weekly report. A fraction of the time.
An illustrative example: the weekly sales report many teams still build by hand.
- Copy data out of emails and PDFs2 hrs
- Paste it into the spreadsheet1.5 hrs
- Check it against the system1.5 hrs
- Write and email the report1 hr
- New email or file arrives, the flow startsauto
- AI extracts, cleans, and cross-checks the dataauto
- Your team reviews only the flagged exceptions15 min
- Report is built and sent automaticallyauto
If your team copies data between tools, writes the same reports every Monday, or replies to the same customer questions by hand, we can usually automate it within a few weeks.
The problems this solves
The situations where clients usually bring us in.
Your team can't scale by hiring alone
Every new customer adds manual work. AI automation means you don't need to hire another ops person just to keep up.
Reports arrive too late to act on
By the time someone compiles the weekly dashboard, the week is over. Automated reports land in your inbox every Monday at 8am.
Context keeps getting lost
Info lives in Gmail, Slack, Notion, Google Drive, a CRM, and three spreadsheets. Agents can read across all of them and act.
Customers expect instant answers
Support tickets that sat in a queue for 4 hours now get a first response in 30 seconds, with an answer that fits the question, not a canned reply.
What's delivered at the end
Every engagement ends with these in your hands.
Automations that run in production
Not a demo in a notebook. Deployed, monitored, with error alerts and retry logic.
Clear ROI tracking
We measure hours saved per workflow per week so you can see exactly what each automation is worth.
Internal documentation + training
Your team knows how to tweak prompts, add steps, or pause a workflow without us.
A prioritized backlog of future wins
During discovery we usually find 10+ automation opportunities. We ship the top ones and hand you the roadmap for the rest.
From first call to live in production
Map the workflow
Shadow your team for a day (remote is fine). Find out where their hours really go.
Prioritize by ROI
Rank each candidate by hours saved × complexity. Pick the top 2-3 for sprint one.
Build & ship in 2-week sprints
One workflow live per sprint. Weekly demos. Each workflow goes to production as soon as it's ready.
Measure & tune
After go-live, we track accuracy, cost per run, and time saved. Tune prompts, add edge cases, prove the ROI.
Under the hood
For the CTO, tech lead, or engineering manager evaluating us: here's how we build.
- New email or file
- Webhooks & forms
- Schedules
- n8n, Make, Zapier
- Python / TypeScript services
- Retries & human review steps
- GPT, Claude, Gemini, open-source
- Agents with MCP tools
- Your docs via retrieval
- CRM, ERP, accounting
- Google Workspace, M365
- Slack, WhatsApp, email
Orchestration
n8n (self-hosted or cloud), Zapier, Make, or custom Python/TypeScript services, whichever fits your ops maturity.
AI Agents & MCP
LangGraph / CrewAI / custom agent loops. MCP servers to give agents structured tool access to your internal systems.
Model layer
Model-agnostic via litellm. Default to GPT-5 / Claude / Gemini. Open-source fallbacks (Llama, Qwen) for privacy-sensitive flows.
Evals & observability
Langfuse or Helicone for traces. Eval sets for every critical prompt. Cost and latency dashboards.
Integrations
REST, GraphQL, webhooks, OAuth, SAML, plus native connectors for Google Workspace, Microsoft 365, HubSpot, Salesforce, Shopify, Xero, QuickBooks, Slack, WhatsApp Business.
Deploy & ops
Docker, Kubernetes, or serverless. CI/CD via GitHub Actions. On-call runbooks so nothing breaks in silence.
You'll walk away with
- Live, production-ready AI workflows running on your stack
- Source code in your repository, full ownership
- Integration credentials, runbooks, and failure playbooks
- Dashboard tracking hours saved, $ saved, and accuracy per workflow
- Team training session + written documentation
- 30-day post-launch tuning included
This is a fit if…
- Ops, sales, finance, or support teams spending 10+ hours/week on repetitive work
- Companies with data scattered across many SaaS tools
- Teams that tried Zapier but hit its limits on conditional logic or AI reasoning
- Leaders who want a measurable return from the first project
Most AI automation engagements are fixed-fee per workflow (typical range $3K–$15K per automation depending on integrations and complexity), or a monthly retainer for ongoing pipeline. We'll quote a clear price before you sign anything.
Questions we hear most often
Is this safe for our customer or financial data?
Yes. We default to zero-retention model APIs, self-hosted inference for sensitive workflows, and field-level PII masking. Every data path is reviewed before go-live. If you're in a regulated industry, we'll work within PDPA, GDPR, and your existing security framework from day one.
Will we be locked into your tooling?
No. You own the code. We deploy to your cloud account, use open standards (n8n, Python, Docker), and leave full docs. If you fire us tomorrow, your team can keep running everything.
How fast can we see results?
Most clients see the first workflow live in production within 3 weeks. Meaningful hours-saved data typically shows up by week 4-5.
What if AI gets the answer wrong?
Every automation has guardrails: confidence thresholds, a human in the loop for edge cases, rollback plans, and eval sets that flag regressions. We design for the cases where it's wrong, not for a perfect model.
We don't have clean data. Does that kill the project?
No. That's the most common starting point. Part of discovery is figuring out what's 'clean enough' for each specific workflow. We rarely need a full data-cleanup project first.