No commitment. You get a written brief with prioritized fixes.
Two lanes under one roof. AI workflows that answer the phone, triage leads, and recap calls. Process automation that kills data entry, syncs your tools, and runs the repetitive jobs your team keeps complaining about. Both measured, both built for operators.
The model or the automation platform is the easy part. The hard part is the workflow around it: the data, the triggers, the fallback, the review gate, the integration into the tools your team already uses. We engineer the workflow, then pick the tool. AI when it needs judgment, automation when it needs a rulebook.
Every workflow we ship has a KPI. Hours recovered per week, leads qualified before a human touches them, rows synced without a human, calls handled after hours, reports generated without a Friday afternoon. If we cannot measure the output, we do not ship the workflow.
Within one business day.
Models drift, APIs change, providers ship policy updates, vendors deprecate endpoints, and your systems evolve. We monitor every workflow we ship on a monthly schedule. Success rate, latency, failure modes, fallback frequency. When something shifts, we catch it before your team does.
Two kinds of work a machine can do for you. The AI kind, and the plumbing kind. Most agencies only sell the shiny one.
You have probably been pitched the AI side: a chatbot dropped on the marketing site, a "ChatGPT for sales" tool nobody opens after week two, a vendor promising "AI-powered everything" who ships a thin wrapper around someone else's API. You have also probably been ignored on the plumbing side: the data your team copies between the CRM and the quoting tool every morning, the report your ops manager builds by hand every Friday, the form submission that still gets forwarded to an inbox and worked by thumb.
Both sides pay back when they are built right. The AI side is where language understanding, voice, and judgment belong. Answering the phone at 2 a.m. Triaging inbound leads by fit before a human opens the inbox. Recapping a sales call into the CRM note your rep would have written. The automation side is where deterministic, repeatable logic belongs. Data copied between systems without a human in the middle. Scheduled syncs that run while the office is closed. Reports generated from live data instead of rebuilt by hand every month.
We build one or two workflows that matter per engagement, under the same roof, with the same rigor. Each one ships with a KPI, a review gate where judgment matters, logging, and a rollback plan. AI when the job needs a brain. Automation when the job needs a rulebook. The boring solution, when the boring solution works.
Clever prompts and clever Zaps both look impressive in a demo and both fall over the first time real data hits them. Shipped workflows are engineered end to end: the data pipeline, the trigger, the fallback, the review gate, the integration into the tool the team already uses, and the logging you need to know it is still working next month. AI when the job needs judgment, automation when the job needs a rulebook. The choice comes last.
"The AI is working" or "the Zap is running" are not metrics. Every workflow we ship has a specific output we can count: recaps written, leads scored before a human touches them, calls handled end to end, rows synced without errors, reports generated on time. We baseline at kickoff, measure monthly, and kill workflows that stop earning their keep.
Models make mistakes. APIs go down. Rate limits hit. Fields get renamed. Production workflows assume all of it. Sensitive tasks sit behind a human review gate. AI output gets validated against a schema before it ships anywhere. Automations retry with backoff, log every run, and escalate when a run looks off. When the workflow is unsure, it pauses and asks instead of guessing.
Discover how much your team is losing to manual, repetitive tasks.
Directional only. The ~80% figure is a working baseline from our typical automation programs. Your actual savings depend on the specific tasks, the data quality, and whether human judgment is required on the output. Audit tells you which ones are worth building first.
Every AI and automation engagement starts with these as the baseline. Not every workflow needs every piece, but the engagement is scoped from this menu. Additional depth layers on top depending on the workflow, the sensitivity of the output, and how much monitoring the business needs once the system is live.
Two-week forensic audit of candidate workflows. Which ones hurt. Which ones have measurable output. Which ones have reliable data. The audit typically kills seventy percent of the list and picks the one or two worth prototyping.
Two to three weeks on real data and real output. We wire the model, the prompt, the fallback, and the human review gate. If the prototype is not paying back, it does not graduate.
Ship inside your existing tools, in your tenancy. Observability, review gates where sensitive, clear escalation paths, and a rollback plan. Four to eight weeks from prototype green light.
Human review gates on sensitive tasks, structured output validation, retrieval grounding where hallucinations would cost you. Every workflow assumes the model will be wrong at some rate and plans for it.
Logging every agent run. Dashboards for success rate, latency, fallback rate, and human overrides. Monthly drift review. When the model provider changes something, we catch it before you do.
Which workflows hurt. Which ones have measurable output. Which ones have reliable data. The audit kills 70% of candidate use cases before we build.
Two to three week prototype of the top use case. Real data, real output, real measurement. If it is not paying back in the prototype it will not pay back in production.
Ship to production with observability, human review gates where needed, and a rollback plan. Typically four to eight weeks from prototype green light.
Specific numbers depend on the workflow, the data, and how the team adopts it. What we commit to is the method and the honesty. Here is what clients feel, quarter over quarter, when the workflow is earning its keep.
This is a generalized example to show the shape of the work. Every engagement starts with a use-case audit across both lanes, AI and automation, and the audit tells us what is actually realistic for your workflows, your data, and your team's appetite for adoption.
Fixed-fee prototype. Fixed-scope production build. Monthly retainer for monitoring and iteration. Model API costs are billed at cost.
Usually both, but most of the payback comes from automation. Data entry, scheduled syncs, and form-to-CRM routing are where teams lose the most hours. AI is the right answer for voice answering, lead triage at scale, and anything that requires language understanding. The audit tells us which is which.
Depends on the workflow. Claude from Anthropic for long-context reasoning, sensitive-content workflows, and anything that requires careful outputs. OpenAI for general-purpose agents and voice. We use the model that fits the job.
Zapier and Make are the right answer for roughly 70% of automation needs. Fast to build, easy to maintain, transparent to your team. Custom code shows up when volume scales, when the logic gets complex, or when the integration needs to be bulletproof. We start with no-code when we can and graduate to code when we must.
AI integrations use the paid API tier with zero retention for training. We never use consumer-tier chat products for production workflows. Automation tools run on your accounts with proper access controls and audit logging. Data handling is documented in the engagement contract.
Every AI workflow assumes the model will be wrong at some rate. Production systems include retrieval grounding (RAG), structured output validation, human review gates, and clear escalation paths for edge cases. Automation flows are deterministic by design, so hallucination is not a concern on that side.
No, and you should run from anyone who tells you it can. Our voice agents handle common inquiries and escalate complex conversations. Automations eliminate busywork. The humans do the work that requires judgment.
Pulled from strategy calls, Slack threads, and end-of-quarter recaps. Names and companies anonymized, voice unchanged.
We had been burned by two previous AI vendors who sold us a chatbot demo and disappeared. These folks started with a use-case audit and killed most of what we asked for. What we shipped is a post-call recap agent that writes the CRM note and flags follow-ups. Our reps get real time back.
Inbound leads used to sit in an inbox for hours before somebody triaged. They built us a lead triage agent that enriches, scores, and routes inside a minute. Humans still approve the outbound copy, but the queue is clean by the time the team shows up.
Voice agent answering after-hours storm calls. Books the inspection if the caller is ready, escalates to a human if the claim is complex. Review gates on anything that touches insurance language. We stopped losing overnight callers to voicemail.
Our ops lead spent every Monday morning copying orders, inventory, and ad spend into a master sheet. They wired up a scheduled automation that pulls all of it into one live dashboard, with error alerts if any source fails. The Monday morning tax is gone.
A 30-minute call. We'll walk your actual numbers and tell you the three things we'd change first.
