Service

AI automation

Chatbots, RAG assistants and agent workflows — wired through n8n or Zapier and scoped to hours or cost you can measure.

Ask ZACBook a consultation
LLM pipelinesRAGAgentsN8NZAPIERCHATBOT
What's included

Scope you can hold us to

01

Use-case scoping against a measurable saving before any model work

02

RAG over your docs, tools and guardrails — not a generic chat widget

03

Custom chatbots and agents with human escalation paths

04

n8n or Zapier orchestration into the apps your team already uses

05

Evaluation sets, cost/latency monitoring and failure alerts

06

Runbook so your team can update knowledge and flows without us

Process

How we deliver ai automation

  1. 01Prove the savingBaseline hours or cost. If the AI feature can't beat it on paper, we don't build it.
  2. 02Grounded prototypeRetrieval over your real docs, with evaluation cases from day one.
  3. 03Wire into operationsn8n or Zapier connects the assistant to CRM, email, tickets and the tools people already open.
  4. 04Measure and tightenDeflection, accuracy and cost per conversation — then iterate.
Start to submission

What that looked like on Opply AI — Student Email Assistant

Opply · Education

  1. Phase 1

    Framed the actual problem

    Students were not short of opportunities — they were short of any way to see them. Scope was set on turning inbox noise into a ranked, trackable pipeline, not on building another inbox.

    DeliveredScope and success criteria

  2. Phase 2

    Wired the intake

    Direct Gmail integration handles automated processing and sync, with manual batch upload alongside it — parsed against real-time progress over WebSockets so a large import stays interactive instead of freezing.

    DeliveredGmail sync and batch upload

    Opply email processing and structured opportunity extraction view
    Extraction pipeline
  3. Phase 3

    Grounded extraction in a schema

    LangChain and Mistral tool-calling extract against fixed schemas, so the output is a typed record the product can rank — not prose the UI has to re-parse.

    DeliveredStructured opportunity records

    Opply AI dashboard showing extracted student opportunities from email
    Opportunity inbox
  4. Phase 4

    Ranked against the student's profile

    A profile-fit system scores each opportunity on eligibility, so what surfaces first is what the student can actually win.

    DeliveredFit ranking across the pipeline

    Opply profile fit ranking view for academic and professional opportunities
    Ranking & fit
  5. Phase 5

    Pushed only what mattered

    High-priority matches go to WhatsApp through Twilio, and async backend jobs keep the app responsive under load. Shipped on Vercel and Render.

    DeliveredWhatsApp alerts and production deploy

    Opply high-priority opportunity alerts delivered via WhatsApp
    WhatsApp alerts
See the full project →
Related projects

Proof in production

All work →
FAQ

About ai automation

Ask ZAC →
Will you just wrap ChatGPT for us?
No. We build retrieval, agents, evaluation and guardrails — then connect them with n8n or Zapier so the answer reaches the right system, not only a chat box.
n8n or Zapier?
Zapier when you want speed and SaaS connectors. n8n when you need self-hosted control, complex branching or lower volume cost. We pick for fit during discovery.
What if our data isn't ready?
Then we say so. Process and data hygiene often come before model work — and that recommendation is free in discovery.
Next step

Not sure this is the right service? Ask ZAC.

Three minutes with ZAC Consultant gets you a recommended solution, a feature list, a timeline and a cost band. Then decide whether you want to talk to us.

  1. 01Describe the problem
  2. 02Get your roadmap
  3. 03Book a consultation
Start with ZACBook a consultation