I automate business ops with teams of AI agents to help you scale w/o increasing overhead | Top Artificial Intelligence Voice | AI Consultant | Founder at Synthoria Labs

Most B2B founders and consultants are not spending enough time on revenue generating activities.

This is because they rely too heavily on the old way of doing everything manually.

So their days are filled with:

→ Managing their pipeline, trying to nurture every lead before they go cold
→ Sifting through Sales Nav, reading endless profiles to build a perfectly targeted list
→ Piecing together contracts/proposals for every client
→ Training yet another employee to try and pick up the slack

How can you focus on growth when 60% of your time is taken up by mind-numbing busywork?

The cycle continues..

Sleepless nights worrying about customer churn rates.

Laying awake wondering if their pipeline will dry up.

It doesn't have to be this way

All of this goes away with an AI Implementation Strategy

And no, I don't mean copying and pasting prompts into ChatGPT hoping it will save you.

Here's my 3 step strategy to reclaim 15 hours every week:

1) Streamlined Sales Funnel System
2) Client Relation Autopilot
3) Internal Productivity optimizer

You don't need more employees. You need the right systems that empower your current team to produce twice the results

...in half the time.

You'll eliminate 60% of the mind numbing work that's holding you back from substantial growth.

The stress of juggling a million tasks is gone, you focus on revenue generating activities, your employees thank you

Join the top 1% of founders leveraging automations to become a business of the future

Check my featured section to see see how you can do it too

Jake George

Building AI systems for business owners to do less and scale better using my bespoke AIM philosophy that improves productivity + top line dollars.

- I’m a data and AI platform architect who designs and delivers production systems end-to-end (architecture + hands-on build + cross-functional execution).
- Recent focus: MLOps, GenAI/RAG, data pipelines, governance, observability, and cost/performance tradeoffs.
- I typically help teams:
  - Stand up data platforms (batch/stream, lakehouse/warehouse, SLAs)
  - Deploy ML/LLM workflows (evaluation, monitoring, rollback, security)
  - Align stakeholders and deliver roadmaps as a program lead / TPM when needed
- Tech: AWS, Azure, Databricks, Spark, Python, CI/CD, Terraform, Kubernetes (only include what you can defend)
- Open to: Data/AI platform architect and TPM (data/AI) roles (contract or FTE)

Elia Ahadi

Governance doesn’t have to slow teams.
I build paved roads instead.

When the safe path is easiest,
teams move faster.

What I try to “pave” first:

---> default data classifications
---> standard access patterns
---> dataset contract templates
---> approved tool allowlists
---> built-in logging and tracing

For example, when a team needs data:
they start from a contract template.
Owner, SLA, and tags are baked in.

Less debate.
Less rework.
More shipping.

What’s the best paved road you’ve seen?

Data/AI platform lead | Solution design + delivery | Databricks, governance, trusted metrics

When an AI agent fails,
I don’t call it “hallucination.”

I bucket failures.
So we can fix the right thing.

My simple taxonomy:
--- Retrieval:  wrong or missing source
--- Data freshness:  source is stale
--- Tooling:  API error, timeout, bad schema
--- Permissions:  access denied, missing role
--- Policy:  blocked by rules or constraints
--- Workflow:  unclear handoff or escalation
--- User intent:  vague request, wrong routing

Different bucket.
Different fix.

What bucket shows up most for you?

Most AI agents aren’t “magic AI.”
They’re workflows with clear boundaries.

I think of autonomy as levels:

-> L1 Assist --- drafts and summarizes
-> L2 Recommend --- suggests actions and reasons
-> L3 Execute with approval --- runs tools, waits for sign-off
-> L4 Execute within limits --- acts inside guardrails
-> L5 Self-directed --- plans and completes end-to-end


The product work is the boundary.
Make it explicit and enforced.

Boundaries look like this:

-> permissions and roles
-> tool allowlists and schemas
-> rate limits and spend caps
-> safe fallbacks and escalation


Where do you draw the line today?
Recommend only, or tool execution too?

When I ship an AI agent,
I set the RACI early.

Without it, incidents get messy.
Fast.

Here’s how I usually split it:
1. Product (me): success metrics, scope, rollout
2. Data owner: definitions, freshness, SLAs
3. Engineering: tools, reliability, deployments
4. Risk/compliance: policy, constraints, approvals
5. Ops/on-call: monitoring, comms, runbooks

Then I make 3 things explicit:

1) who approves changes
2) who owns failures
3) who can shut it off

If answers go stale,
I don’t blame the model first.
I check freshness and ownership.

How do you assign ownership today?
Central, domain, or hybrid?

Most data platforms don’t fail because of the stack.
They fail because ownership is unclear.

3 questions I ask early:

Who owns metric definitions?
Who approves breaking schema changes?
Who owns data incidents + SLAs?

If those are fuzzy, trust drops fast, no matter how modern the tooling is.

What’s worked best for you 
-> central, domain, or hybrid?

The MVP nobody wants to build:
a repeatable test suite for your AI agent.

Think “unit tests,” but for AI.
Same tasks, every release.
Same scoring, every time.

My minimum setup:

1 --- 20–50 real “golden” tasks
2 --- clear pass or fail rules
3 --- failure reasons tagged (why it failed)
4 --- cost and latency tracked
5 --- run it before every deploy

This changes everything.
Faster debugging when it breaks.
Safer upgrades over time.


Do you have this kind of test suite?
Or is it still manual today?

Most AI agent outages
look like “model issues.”

They’re usually:
---> retrieval issues
---> tool issues
---> data freshness issues
---> permission issues

Tracing exposes that.
Fast.

What failure shows up most for you?

The worst human-in-loop design:
approve every step.

I prefer an escalation ladder:
--- agent executes low-risk
--- agent asks on high-risk
--- agent escalates on uncertainty

This keeps speed.
And adds control.

What’s your approval strategy?

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The Battle

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Engagements

Likes

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