01 — Gravity
Every vendor wants to be the hub
SaaS vendors are not neutral. Their AI features create new reasons to keep your data, decisions, and daily work inside their system.
Services • AI & LLM Consulting
AI value without turning your business into a vendor's AI silo.
Practical consulting for smaller businesses that want useful AI in real workflows: fast first wins, sober technical judgment, and a client-owned platform that keeps you free to choose the right tools.
The current reality
The problem is not too little AI. It is too many would-be AI hubs.
Every app is adding a chat box, assistant, copilot, or embedded AI layer. Some are useful. Many are narrow. Almost all are designed to make that vendor the center of your workflow.
02 — Silos
Point assistants rarely see the whole business
A CRM assistant knows the CRM. A ticketing assistant knows tickets. Your business process usually lives between systems, not inside one.
03 — Action
Strategy without delivery is just another meeting
The useful work is specific: connect a workflow, transform the data, make a decision, route the result, and measure whether it helped.
The preferred model
An agnostic AI platform where the client owns the center.
The goal is not to replace every vendor tool. The goal is to keep those tools useful without letting any one of them become the permanent AI gatekeeper for your business.
- Use best-fit models, APIs, and tools without hardwiring the business to one vendor.
- Keep core workflow logic in a client-controlled hub, not scattered across app-specific AI features.
- Let AI make routing, transformation, and enrichment decisions that rigid integrations cannot.
- Build human approval points where judgment, risk, or customer trust matters.
Client-owned hub, vendor-neutral edges.
Source systems
CRM, email, forms, tickets, finance
Decision layer
LLMs, rules, retrieval, validation
Business action
Route, draft, reconcile, escalate, update
Human control
Review, approve, audit, improve
Where value starts
Small workflows that return value before the organization loses patience.
The first useful AI work is usually not a moonshot. It is a messy workflow where humans copy, interpret, reformat, route, and chase information across systems.
01 — Intake
Classify and enrich incoming work
Read emails, forms, tickets, and documents; identify intent; pull missing context; and prepare the next step for a human or system.
02 — Routing
Make smarter handoff decisions
Choose where data should go based on content, customer state, policy, urgency, and exceptions instead of brittle field mappings.
03 — Transformation
Turn messy data into usable records
Normalize language, extract structured fields, reconcile partial records, and prepare clean updates for the systems that need them.
04 — Augmentation
Add intelligence around existing apps
Improve tools you already own without waiting for a vendor's limited integration roadmap or paying for a forced AI upgrade tier.
05 — Review
Keep people in the right loop
Route low-risk work automatically and bring humans in when the system sees ambiguity, policy risk, money movement, or customer impact.
06 — Measurement
Prove the return quickly
Track time saved, error reduction, faster response, reduced rework, and better data quality before expanding the platform.
How engagements work
Start small. Ship something useful. Expand only when the evidence says to.
This is not AI cheerleading, and it is not a deck-first strategy exercise. It is a delivery-oriented engagement that starts with one high-friction workflow and builds outward from working software.
01 — Find
Pick a workflow with visible drag.
We look for repetitive handoffs, manual interpretation, duplicate entry, delayed routing, or places where good employees are acting as glue between systems.
02 — Build
Create the smallest owned AI path that can work.
That may be retrieval, an LLM decision step, structured extraction, API orchestration, a review queue, or a thin internal interface.
03 — Measure
Judge it by business behavior, not novelty.
We measure whether the workflow became faster, cleaner, less error-prone, easier to audit, or less dependent on tribal knowledge.
04 — Expand
Turn the useful pattern into a durable platform.
Once one workflow proves value, the same client-owned hub can connect more systems, models, approvals, and data without handing control to a single vendor's AI roadmap.
Start practical
Bring one workflow that feels too manual, too slow, or too dependent on one person.
A short conversation is enough to decide whether AI belongs in that workflow, where the first return might show up, and what should stay firmly under human control.