Your team is spending 40% of their time on tasks a machine should handle.
We audit your workflows, identify the highest-impact automation opportunities, and deploy working solutions using proven tools (Zapier, Make, n8n, and an AI layer where it matters) in as few as 15 days.
Manual work scales linearly. Your business shouldn't.
Your best people are doing your worst work
That operations manager you're paying INR 15 Lakh a year? She's spending three hours a day copying data between spreadsheets, chasing invoice approvals over email, and manually generating reports that nobody reads until Thursday's standup. She was hired to think strategically. Instead, she is 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 still sit untouched for 48 hours because nobody has a system to route, score, and follow up automatically. Your HR team onboards new hires with a checklist on Google Docs. Your finance team reconciles invoices in Excel. You know there's a better way, but a full custom software build feels like overkill for problems that existing tools can solve. Because they can.
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 today is a recurring cost that rarely shows up as a line item, so it stays invisible. A single data entry workflow that takes one employee two hours a day costs you roughly INR 5 Lakh per year in loaded salary alone, before accounting for the errors that follow, the rework those errors generate, and the downstream bottlenecks nobody is tracking because they've become background noise. Multiply that across five or ten workflows and the number is no longer trivial. It's a hiring decision you're making every year without realising it.
Audit. Design. Build. Hand over. 15 days.
We don't sell you a platform or lock you into proprietary tools. We use 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.
Process Audit
Days 1-3
We map every workflow in scope end to end: who does what, in which tool, how often, and where the bottlenecks are. We measure time spent, error rates, and downstream impact. We interview the people actually doing the work, not just the managers who think they know how it works. The output is a prioritized list of automation opportunities ranked by time saved, error reduction, and implementation complexity.
Deliverable: Process audit document with workflow maps, time/cost analysis, and prioritized automation opportunity list
Solution Design
Days 4-5
For each automation we're building, 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 anything. No surprises, no scope drift.
Deliverable: Solution architecture document with tool selection, workflow diagrams, integration specs, and edge case handling
Build & Test
Days 6-12
We build every automation, connect it to your live systems, and test it thoroughly, not just the happy path, but edge cases, error states, and failure modes. Each automation is tested with real data from your environment. We don't move to the next workflow until the current one is fully working and stable. Where AI adds value (document parsing, email classification, intelligent routing) we layer it in using proven APIs, not experimental models.
Deliverable: Fully built and tested automations running in your environment with test results documentation
Launch & Handover
Days 13-15
We deploy to production, run parallel testing with your team for 48 hours, and conduct hands-on training for every person who'll interact with the automations. You get complete documentation: what each automation does, how it's triggered, what to check if something fails, and how to modify it without calling us. We also set up monitoring and alerting so you'll know within minutes if an automation fails, not three days later when someone notices the data stopped flowing.
Deliverable: Live automations deployed to your environment, training sessions (recorded), complete documentation, monitoring and alerting setup
Process Audit
Days 1-3
We map every workflow in scope end to end: who does what, in which tool, how often, and where the bottlenecks are. We measure time spent, error rates, and downstream impact. We interview the people actually doing the work, not just the managers who think they know how it works. The output is a prioritized list of automation opportunities ranked by time saved, error reduction, and implementation complexity.
Deliverable: Process audit document with workflow maps, time/cost analysis, and prioritized automation opportunity list
Solution Design
Days 4-5
For each automation we're building, 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 anything. No surprises, no scope drift.
Deliverable: Solution architecture document with tool selection, workflow diagrams, integration specs, and edge case handling
Build & Test
Days 6-12
We build every automation, connect it to your live systems, and test it thoroughly, not just the happy path, but edge cases, error states, and failure modes. Each automation is tested with real data from your environment. We don't move to the next workflow until the current one is fully working and stable. Where AI adds value (document parsing, email classification, intelligent routing) we layer it in using proven APIs, not experimental models.
Deliverable: Fully built and tested automations running in your environment with test results documentation
Launch & Handover
Days 13-15
We deploy to production, run parallel testing with your team for 48 hours, and conduct hands-on training for every person who'll interact with the automations. You get complete documentation: what each automation does, how it's triggered, what to check if something fails, and how to modify it without calling us. We also set up monitoring and alerting so you'll know within minutes if an automation fails, not three days later when someone notices the data stopped flowing.
Deliverable: Live automations deployed to your environment, training sessions (recorded), complete documentation, monitoring and alerting setup
Working automations, not a recommendations deck.
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)
- Fully built automations connected to your live systems
- AI layer integration 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 testing
- Hands-on training sessions for your team (recorded for future onboarding)
- Complete documentation: triggers, logic, failure modes, modification guides
- Monitoring and alerting configuration for every automation
We automate existing workflows. These are different engagements.
We scope tightly to keep timelines honest and results measurable. Each of these is available as a separate service.
Custom AI tool development or agentic workflows
If your automation needs go beyond existing tools (custom-trained models, multi-step AI agents, or bespoke software) that's a build engagement with a different scope, timeline, and team.
Custom AI Tool DevelopmentAI strategy, diagnostic, or opportunity assessment
If you're not sure where automation fits in your broader AI roadmap, the diagnostic will tell you. It maps every AI and automation opportunity in your business and tells you which ones to pursue first.
AI Strategy & DiagnosticOngoing monitoring, maintenance, and enhancement of deployed automations
After we hand over, you may want ongoing support: bug fixes, enhancements as your workflows evolve, and proactive monitoring. That's covered under a separate retainer.
AI Operations & Managed SupportIs this the right fit?
Right for you if
- You're a mid-market company (50-500 employees) with manual, repetitive workflows that eat up significant team hours every week, and you want them gone, not just improved.
- You've tried piecemeal automation before (a Zapier here, a script there) and it broke, went undocumented, or never covered the full workflow. You need someone to do it systematically.
- 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 that 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 as an off-the-shelf tool. That's our Custom AI Tool Development service.
- You're not sure which processes to automate or whether AI is the right investment. Start with our AI Strategy & Diagnostic to identify the highest-value opportunities first.
Real workflows. Real time saved.
Problem
A D2C brand processing 800+ orders per day was spending 4 hours daily on order status updates, inventory sync between Shopify and their warehouse system, and manual returns processing. Customer complaints about delayed status updates were increasing.
What we did
Built an 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; and an AI-powered returns classification system that auto-approved straightforward returns and flagged edge cases for human review.
Outcome
Order status update complaints dropped by 90%. Returns processing time reduced from 5 days to 8 hours. The operations team reclaimed 20+ hours per week, redeployed to supplier negotiations and demand planning.
Problem
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. With 15-20 proposals per month, it consumed nearly a full-time senior resource.
What we did
Automated the entire proposal pipeline: an AI layer that pulls relevant case studies and credentials from past proposals based on the prospect's industry and requirements, auto-populates the pricing model from a rules engine, routes the draft for internal review with automated reminders, and tracks approval status in a central dashboard.
Outcome
Proposal generation time dropped from 6-8 hours to 90 minutes. Approval cycle shortened from 5 days to 2 days. Win rate improved by 12% because proposals were more consistently branded and included more relevant case studies.
Problem
An NBFC with INR 500 Cr in AUM was spending 30+ analyst hours per week on regulatory reporting, pulling data from six different systems, reconciling mismatches, formatting into compliance templates, and manually routing for sign-off.
What we did
Built automated data pipelines that pull from all six source systems on a scheduled basis, reconcile and flag discrepancies automatically, populate regulatory report templates, and route for sign-off with full audit trail. An AI layer handles anomaly detection, flagging data points that deviate from historical patterns for human review.
Outcome
Reporting time reduced from 30+ hours to 3 hours per week. Data discrepancies caught 48 hours earlier on average. The compliance team now spends their time on analysis and risk assessment instead of data assembly.
What a business automation engagement looks like end to end.
20+ hours per week reclaimed, 90% reduction in order status complaints
A D2C brand doing INR 80 Cr in annual revenue engaged us to automate their order operations. Their three-person ops team was spending the majority 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 identified 14 discrete manual tasks across three systems. The solution design took 2 days. We selected Make as the primary automation platform because of its reliability with e-commerce integrations, layered in 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), followed by order notifications, and finally returns processing. Each automation was tested with live data before moving to the next. Launch and handover took 3 days, including parallel running, team training, and documentation. Total investment: INR 4.5 Lakh. Estimated annual savings: INR 18 Lakh in labor costs alone, before accounting for reduced customer complaints and faster returns processing.