Stop paying salaries for work software should do.
Business automation for mid-market companies. Workflow automation, document processing, reporting dashboards, AI chatbots, and CRM automation deployed in 15 days. $5-$50K per project.
The Problem
Manual work scales linearly. Your business shouldn't.
Your best people are doing your worst work: That operations manager you pay $90,000 a year spends three hours a day copying data between spreadsheets, chasing invoice approvals, and building reports nobody reads until Thursday's standup. You hired her to think about the business. Instead, she's a human middleware layer between systems that should already talk to each other.
You've outgrown your tools but can't justify a custom build: Your CRM has a lead form, but leads sit untouched for 48 hours because nobody built routing and follow-up automation. HR onboards with a Google Docs checklist. Finance reconciles invoices in Excel. A full custom build feels like overkill for problems that existing tools solve. And they do.
Automation attempts that created more work: Someone set up a few Zapier workflows last year. They broke within a month, nobody documented them, and now your team doesn't trust automation at all.
The cost compounds every month you delay: Every manual process you tolerate is a recurring cost that rarely shows up as a line item. A single data entry workflow that takes one employee two hours a day costs roughly $30,000 per year in loaded salary. That's before errors, rework, and bottlenecks nobody tracks. Multiply that across five or ten workflows and you're making a hiring decision every year without realizing it.
Our Approach
Audit. Design. Build. Hand over. 15 days. We don't sell you a platform or lock you into proprietary tools. We pick the best automation stack for your use case, build it to last, and hand over documentation thorough enough that you never need to call us to understand what you own.
Phase 1 — Process audit (Days 1-3): We map every workflow in scope end to end: who does what, in which tool, how often, where things stall. We measure time spent, error rates, and downstream costs. We interview the people doing the work, managers and ICs who know how it really runs. Output: a prioritized list of automation opportunities ranked by time saved, error reduction, and complexity. Deliverable: Process audit document with workflow maps, time/cost analysis, and prioritized automation opportunity list
Phase 2 — Solution design (Days 4-5): For each automation, we select the right tool (Zapier, Make, n8n, or custom scripts with an AI layer), design the workflow logic, define triggers and conditions, map data flows between systems, and identify edge cases. You review and approve the design before we build. Deliverable: Solution architecture document with tool selection, workflow diagrams, integration specs, and edge case handling
Phase 3 — Build & test (Days 6-12): We assemble every automation, connect it to your live systems, and test it: happy path, edge cases, error states, failure modes. Each automation runs against data from your environment. We move to the next workflow only after the current one is stable. Where AI adds value (document parsing, email classification, intelligent routing), we add it with production-grade APIs and models. Deliverable: Built and tested automations running in your environment with test results documentation
Phase 4 — Launch & handover (Days 13-15): Production deployment, 48-hour parallel test with your team, and practical training for everyone who'll use the automations. Full documentation: what each automation does, how it triggers, what to check on failure, how to modify it without calling us. Monitoring and alerting included so you know within minutes if something fails, not three days later when someone notices the data stopped. Deliverable: Live automations deployed to your environment, training sessions (recorded), full documentation, monitoring and alerting setup
Deliverables
Audit & Design (Days 1-5)
- Process audit with end-to-end workflow maps and time/cost analysis for every process in scope
- Prioritized automation opportunity list ranked by ROI and implementation complexity
- Solution architecture with tool selection, workflow logic, and integration specifications
Build & Test (Days 6-12)
- Built automations connected to your live systems
- AI layer where applicable (document parsing, classification, intelligent routing)
- Test results and edge case documentation for every automation
Launch & Handover (Days 13-15)
- Production deployment with 48-hour parallel test
- Practical training for your team (recorded for future onboarding)
- Full documentation: triggers, logic, failure modes, modification guides
- Monitoring and alerting for every automation
Who This Is For
Right for you if: You're a mid-market company (50-500 employees) with manual, repetitive workflows that eat up team hours every week, and you want them gone.. You've tried piecemeal automation before (a Zapier here, a script there) and it broke, went undocumented, or never covered the full workflow. You want it done properly.. You need results in weeks, not quarters. Your problem is operational efficiency, not fundamental technology, and you don't want to pay custom development prices for something existing tools can solve..
Not right if: You need a custom AI system: a trained model, an agentic workflow, or bespoke software that doesn't exist off the shelf. That's our custom AI tool development service.. You're unsure which processes to automate or whether AI is the right investment. Start with our AI strategy & diagnostic to find the highest-value opportunities first..
Use Cases
E-Commerce / D2C: A D2C brand processing 800+ orders per day spent 4 hours daily on order status updates, inventory sync between Shopify and their warehouse system, and manual returns processing. Customer complaints about delayed updates were growing. — Built end-to-end order operations automation. Real-time inventory sync between Shopify, the warehouse WMS, and the accounting system. Automated order status notifications via WhatsApp and email triggered by warehouse scan events. AI returns classification that auto-approved straightforward returns and flagged edge cases for human review.. Outcome: Order status complaints dropped 90%. Returns processing went from 5 days to 8 hours. The ops team reclaimed 20+ hours per week and shifted to supplier negotiations and demand planning.
Professional Services: A 200-person consulting firm was drowning in proposal generation. Partners spent 6-8 hours per proposal pulling data from past projects, customizing templates, and routing for internal approvals. At 15-20 proposals per month, it consumed nearly a full-time senior resource. — Automated the full proposal pipeline. An AI layer pulls relevant case studies and credentials from past proposals based on the prospect's industry and requirements, auto-populates pricing from a rules engine, routes the draft for internal review with automated reminders, and tracks approval status in a central dashboard.. Outcome: Proposal generation went from 6-8 hours to 90 minutes. Approval cycle shortened from 5 days to 2. Win rate improved 12% because proposals were more consistently branded with more relevant case studies.
Financial Services: An NBFC with $60M in AUM spent 30+ analyst hours per week on regulatory reporting: pulling data from six systems, reconciling mismatches, formatting compliance templates, and manually routing for sign-off. — Built automated data pipelines that pull from all six source systems on schedule, reconcile and flag discrepancies automatically, populate regulatory report templates, and route for sign-off with full audit trail. An AI layer detects anomalies and flags data points that deviate from historical patterns for human review.. Outcome: Reporting went from 30+ hours to 3 hours per week. Data discrepancies caught 48 hours earlier on average. The compliance team now spends time on analysis and risk assessment instead of data assembly.
Results
One engagement, end to end
E-commerce operations automation: 20+ hours per week reclaimed, 90% drop in order status complaints. A D2C brand doing $10M in annual revenue hired us to automate their order operations. Their three-person ops team spent most of each day on manual work: syncing inventory across Shopify and their warehouse system, sending order status updates to customers, and processing returns. The process audit took 3 days. We mapped every workflow, timed each step, and found 14 discrete manual tasks across three systems. Solution design took 2 days. We picked Make as the primary automation platform for its reliability with e-commerce integrations, added an AI classification model for returns triage, and designed the WhatsApp notification flow. The build took 7 days. We deployed inventory sync first (highest-risk, highest-impact), then order notifications, then returns processing. Each automation was tested with live data before we moved on. Launch and handover took 3 days, including parallel run, team training, and documentation. Total investment: $5,500. Estimated annual savings: $22,000 in labor costs alone, before reduced complaints and faster returns processing.
Frequently Asked Questions
How long does a business automation project take?
1-6 weeks depending on complexity. Simple (1-2 workflows): one week. Medium (3-5 automations across multiple systems): 2-3 weeks. Full department automation with AI layer: 3-6 weeks. Every engagement follows the same structure: process audit (days 1-3), solution design (days 4-5), build and test (days 6-12), launch and handover (days 13-15). Larger engagements extend the build phase; the audit and design stay the same.
What tools do you use for automation?
We pick tools based on your use case, not our preferences. Primary platforms: Zapier (simple, high-reliability integrations), Make (complex multi-step workflows with conditional logic), n8n (self-hosted environments with data sensitivity requirements). For AI-augmented automations, we add Claude API, OpenAI, or vision models for document processing, classification, and intelligent routing. We never lock you into proprietary tools. Everything we build runs on platforms you can manage independently.
What's the difference between this and custom AI development?
Scope and cost. Business automation uses existing tools and platforms to connect your systems and eliminate manual work. It's faster and cheaper. Custom AI development builds software from scratch: trained models, agentic workflows, custom interfaces. If your problem can be solved by connecting existing tools well, you want this service. If you need software that doesn't exist yet, you want custom AI tool development. The process audit will make the distinction obvious.
Will my team need technical skills to maintain the automations?
No. Every automation comes with plain-language documentation: what it does, how it triggers, what to check when something goes wrong. We run practical training with every person who'll use the system. Zapier, Make, and n8n are designed for non-technical users to monitor and modify. If you want ongoing professional management, we offer a managed support retainer through our AI operations service.
What happens if an automation breaks after you hand it over?
Every automation includes monitoring and alerting. You'll know within minutes if something fails. The documentation includes troubleshooting guides for common failure modes (API rate limits, changed field names, expired credentials). For the first 48 hours after launch, we run in parallel and resolve issues live. After that, you can maintain it yourself using the documentation, or engage us on a managed support retainer.
Can you automate processes that involve multiple departments?
Yes. Cross-department automations often deliver the highest ROI because they close the handoff gaps where work stalls. Common example: automating the flow from sales (lead captured in CRM) to finance (invoice generated) to operations (onboarding triggered) to customer success (check-in scheduled). These workflows touch 3-4 systems and usually require 2-3 people to manually move data between them. The entire chain is automatable.
Do you work with our existing CRM / ERP / tools?
Yes. We build on top of your existing stack. We've automated workflows with Salesforce, HubSpot, Zoho, SAP Business One, Google Workspace, Microsoft 365, Slack, WhatsApp Business API, Shopify, WooCommerce, Klaviyo, and dozens more. Healthcare: Epic, Cerner, Athenahealth. Manufacturing: Epicor, Infor, Siemens Opcenter. If your tool has an API or webhook, we can integrate it. The process audit maps your tool environment before we design anything.
What if we want to add more automations later?
Two options. You can engage us for a new automation project where we skip the audit parts that overlap with what we already know and focus on new workflows. Or you sign up for our AI operations & managed support retainer, which includes monthly enhancement hours for adding new automations and optimizing existing ones. Most clients who start with a focused automation project expand scope within 3-6 months.
