Lots of hype, no plan, scattered pilots.
What we did
- Ran an opportunity + risk assessment
- Prioritized a roadmap by ROI
- Stood up a governed first use case
Result Replaced scattered experiments with one funded roadmap and a working pilot.
AI Consulting
AI Automation & Workflows
Put your repetitive, high-volume work on autopilot, with humans in the loop where it counts.
What it is
Automate the busywork, keep the judgment.
We map the processes eating your team's hours and rebuild them as reliable, monitored automations, connected to the tools you already use.
Core services include
- Process discovery and mapping
- Workflow automation design
- Tool and API integration
- Human-in-the-loop checkpoints
- Monitoring, logging, and alerting
- Iteration and optimization
Definition
What is AI Automation & Workflows?
AI Automation & Workflows is the practice of chaining your repetitive, high-volume tasks into software-driven workflows, where AI handles the reading, sorting, drafting, and routing, and a person approves the steps that carry real risk. The goal is to run routine work on autopilot while keeping humans in the loop where judgment matters.
How it works
We map an existing process end to end, then rebuild it as a workflow in tools like n8n, LangGraph, or CrewAI that connects your CRM, inbox, billing, and support systems. AI steps read, classify, and draft; deterministic rules handle the rest; and human-approval checkpoints, retries, and audit logs sit at every point where a mistake would be costly.
Who it’s for
For teams drowning in manual, repeatable work, intake, data entry, triage, routing, follow-ups, report assembly, across ops, support, sales, and finance. The outcome is efficiency and time saved: hours of routine work removed, fewer manual errors, faster turnaround, and cleaner data, so staff spend their time on the judgment calls software cannot make.
In practice
A company receives inbound requests by email all day. A workflow reads each message, pulls the key details, creates the record in the CRM, drafts a reply, and routes anything ambiguous or high-value to a human for approval before it sends; turning a manual queue into a monitored pipeline that runs on its own.
Common automations.
- Lead intake, routing, and enrichment
- Document processing and data entry
- Reporting and dashboard generation
- Customer and internal Q&A
- Follow-up and nurture sequences
See if AI Automation & Workflows is the right move for your team.
Request a free quoteBuild, manage, run
We Build and Operate Your AI Workflows
We build hands-on automation that runs your real business processes, then we manage it day to day. Using LangGraph, CrewAI, and n8n alongside Claude, GPT, and open models, our senior engineers connect your CRM, billing, support, and data systems into workflows built for production reliability at enterprise scale. For managed engagements, this is production automation we own and operate day to day; we can also hand off a documented, owned-by-you workflow with training.
For managed engagements, we monitor workflows in production and remediate within the agreed support scope when an upstream system changes, so automation keeps running reliably without your team babysitting it.
- We design and ship end-to-end workflows across CRM, support, finance, and ops with n8n, LangGraph, and CrewAI
- We wire AI steps into real systems with retries, human-in-the-loop checkpoints, and audit logs
- We handle versioning, monitoring, and error handling so automations stay reliable at scale
- We tune model choice per step, from Claude and GPT to Llama, Qwen, and Mistral, for cost and accuracy
See it in action
The busywork runs itself. You just tap approve.
Workflow monitor · Your Brand
● 7 automations live
Invoice intake & coding
Inbox → extract → QuickBooks · 48 runs
Lead enrichment & routing
Webform → enrich → CRM → Slack · 61 runs
Refund & credit triage
Helpdesk → policy check → payout · 9 runs
$540 refund over the $250 auto-limit, held for one-tap approval
118 runs this week94% zero-touch~29 hrs handed back
Illustrative example, styled to show the kind of output we deliver.
Selected work
Representative engagements.
Turning AI hype into a funded plan and a working first use case.
Wanted answers grounded in their own docs, not hallucinations.
What we did
- Built a RAG system over their knowledge base
- Added citations + an evaluation harness
- Access controls + monitoring
Result Deflected common tickets with sourced answers the team could trust.
Examples are anonymized to honor client NDAs and edited to illustrate typical scope, outcomes vary by market, budget, and starting point.
How & why it works
Automations that survive real data, not just the demo.
We rebuild a repetitive process as an event-driven workflow that machines run and humans supervise: a trigger fires, each step does one well-defined job, and anything low-confidence or high-stakes pauses for a person before it acts. Reliability comes from the plumbing underneath, idempotency, retries, and monitoring, not from hoping the happy path consistently holds.
- Map and score the processWe shadow the actual workflow and document each step, input, and system it touches, then score tasks by volume, repeatability, and error cost. High-frequency rules-based work gets automated; genuine judgment calls stay human or become approval checkpoints, so we automate the right things instead of everything.
- Wire triggers and connect the stackWe connect your existing tools (CRM, help desk, email, sheets, databases) through their APIs and webhooks, and orchestrate the logic in a framework like n8n, LangGraph, or CrewAI. We prefer event triggers and webhooks over polling so work starts the moment something happens, not on a slow timer.
- Add structured extraction and guardrailsWhere a step reads messy input, an LLM extracts fields against a strict schema that gets validated before any write, so malformed output is caught, not committed. Each step carries a confidence threshold plus dollar or risk limits; anything below the line routes to a human-in-the-loop card in Slack or the CRM for one-tap approval.
- Engineer for failure, not the happy pathEvery write is made idempotent with a dedup key so a retry can't double-post; transient API errors retry with backoff, and anything that still fails lands in a dead-letter queue for review instead of silently vanishing. This is the difference between a workflow that survives real load and one that only worked in the demo.
- Monitor, measure, and tuneWe ship per-run logging, tracing, and alerting so you can see run count, zero-touch rate, retries, and exceptions on a dashboard. As volume shifts and edge cases appear, we tighten prompts, thresholds, and rules, and widen what runs unattended as accuracy proves out.
Worked exampleA B2B services firm processing ~400 inbound vendor invoices a month by hand across email and QuickBooks.
- Built an intake workflow: inbox webhook to LLM field extraction (vendor, PO, line items, totals) validated against a strict schema before anything writes
- Added a 3-way match against the PO and receipt, with a confidence-and-dollar gate routing exceptions to a Slack approval card instead of posting blind
- Wired idempotency keys on invoice number plus retries with backoff and a dead-letter queue, so a QuickBooks API hiccup retries instead of double-posting
- Result: roughly 85% of invoices posted zero-touch, exceptions surfaced in minutes not days, and no duplicate entries across the first few hundred runs
Why it works
Automation compounds because the fixed cost of designing a step is paid once, then amortized over every run, while a human pays the full cost every single time. The naive version fails not because the happy path is hard but because real data is messy and systems go down mid-run; durable idempotency, retries, and dead-letter queues turn those inevitable failures into recoverable events rather than corrupted records. Keeping a person on only the high-stakes or low-confidence slice is what lets the other 80-plus percent run unattended with trust, you buy back the routine hours without betting the expensive mistakes on a model.
FAQ
Questions, answered.
We start with a process discovery session where we map the work your team actually does, then score each task by volume, repeatability, and error cost. Tasks that are high-frequency and rules-based get prioritized, while anything requiring real judgment stays human or becomes a human-in-the-loop checkpoint. For example, lead intake and enrichment usually automates cleanly, while approving a discount over a threshold gets routed to a person for sign-off.
It is a deliberate pause where the automation hands a decision to a person before continuing, instead of acting blindly. We place these wherever a wrong move is expensive or hard to reverse, and surface the choice in a tool your team already uses like Slack, email, or your CRM. For example, an AI agent can draft and enrich a customer reply, but a rep approves or edits it in one click before it sends, so you get the speed without losing control of the message.
Every workflow we ship includes monitoring, logging, and alerting plus a defined fallback path, so a failure routes the task back to a human or a retry queue rather than silently dropping it. You get alerted when something needs attention, and the logs tell us exactly where it stopped. This is the difference between an automation that works in a demo and one that runs every day under real data and real load.
Yes. We build on top of your existing stack rather than asking you to replace it, integrating through APIs and connectors to your CRM, help desk, spreadsheets, databases, and internal apps. We assemble these with orchestration frameworks like n8n, LangGraph, and CrewAI so the logic is maintainable, not a pile of brittle scripts. If a system has no clean API, we will tell you upfront and design around it.
Either way, we make that explicit before we build. NYFTY can hand off a documented, owned-by-you workflow with training, or we can manage and run it as an ongoing service, watching the monitoring, tuning prompts and rules, and handling edge cases as your volume changes. Most clients start with us running it while the automation stabilizes, then decide on long-term ownership once it is proven in production.
Let’s make it measurable.