Discover
Map workflows and identify the ones suitable for automation.
Custom apps and AI agents
Convert high friction processes into seconds
We help teams migrate work into apps or AI agents (tools), starting with a pilot.
Companies do not need more AI hype.
They need the right kind of automation: reliable, scalable solutions with clear guardrails and human review gates where judgment or signoff is needed.
What we do
From AI interest to operational deployment.
Prioritize
Score by impact, risk, complexity, and time-to-value.
Implement
Build a bounded pilot with human review, escalation paths, auditability, and clear controls.
Optimize
Measure ROI, improve quality, tune cost, ensure reliability, and expand to additional workflows when proven.
Best fit
Built for repetitive digital workflows with real business constraints.
Good candidates are high-volume, rules-heavy, low-to-medium judgment workflows with clear inputs, outputs, and exception paths.
Customer support triage
Route, classify, summarize, and escalate requests faster without removing human judgment.
Example
Inbound tickets are tagged by issue type, urgency, account value, and sentiment, then routed with a draft response and escalation reason.
Team chat bot integrations
Add bots to Slack, Teams, or internal chat to route tickets, answer questions, and trigger routine tasks.
Example
A team member asks a channel bot for a customer status update; it checks connected systems, answers with source links, and creates a ticket when follow-up is needed.
Document extraction & generation
Extract data from PDFs, files, and email attachments, then generate or file the next document through a custom app.
Example
A driver application PDF is uploaded, AI extracts the structured fields, missing items are reviewed in-app, and a draft qualification file is generated for download.
Sales ops & lead qualification
Enrich CRM records, qualify inbound leads, and prepare next actions.
Example
New leads are enriched from public sources, scored against fit criteria, and queued with a suggested follow-up task.
Meeting follow-up
Extract decisions, tasks, owners, and deadlines from calls and notes.
Example
After a client call, decisions and action items are pushed into the project system with owners, due dates, and open questions.
Finance/admin intake
Classify requests, check completeness, and prepare human approval steps.
Example
Expense and vendor requests are categorized, checked against policy, and prepared for review with missing information flagged.
Offers
Pick the package that matches your current workflow stage.
AI Workflow Discovery Sprint
For teams that want to use apps or AI agent tools, but need help finding the right workflow first.
- Workflow interviews
- Suitability assessment
- Opportunity ranking
- ROI estimate
- Pilot recommendation
Pilot Implementation Sprint
For companies with a likely workflow in mind and a clearer goal, but where the workflow still needs review before building.
- Workflow validation
- Scope and risk review
- Prototype or bounded pilot
- Human review points
- Success metrics
Custom Workflow Build
For teams that know exactly what should be implemented because the wasted time is already obvious.
- Custom app or agent tool
- System integrations
- Document or ticket flows
- Permissions and audit trail
- Launch support
Support & Optimization
For teams that own the live workflow, but want help keeping it stable, useful, and improving after launch.
- Post-launch support
- Bug fixes and improvements
- Prompt and flow tuning
- AI-assisted help and triage
- Further rollout planning
FAQ
Questions worth asking.
Is this just AI consulting?
No. The work ends in something usable: a workflow map, a pilot, a custom app, an agent tool, or a rollout plan. The point is less talking about AI and more removing real operational drag.
What if we already know what we want built?
Good. Then we spend less time discovering the opportunity and more time checking the workflow, edge cases, data, integrations, and launch path before building.
How does a pilot usually look?
We pick one repetitive workflow, map how it works today, build a small working app or AI agent tool around it, and measure whether it is worth scaling.
What are agent tools?
MCP integrations can provide the tools that let users control existing systems through natural language, while allowing an AI agent to safely read from or act inside approved systems. For example, an agent could look up a customer record, create a ticket, draft a document, or trigger an internal workflow with the right permissions and review steps in place.
How do you handle sensitive data and privacy?
We design deployments around the client's data requirements. Where needed, models can run through managed enterprise providers such as AWS Bedrock, so prompts and outputs are not used for model training.
Who maintains the system after launch?
The client should own the workflow day to day. Subtrakt can stay involved through a support plan for fixes, workflow changes, AI-assisted triage, and rollout support.
Do we need to commit to a big project?
No. A good first project should be narrow enough to ship, measure, and judge. If it works, the next rollout is easier to justify.
What kind of work is usually a bad fit?
Workflows are usually a poor fit when ownership is unclear, exceptions are undocumented, required data is incomplete, or decisions cannot be described consistently. These workflows can often be improved, but they usually need process cleanup before automation.