What does Built-N-AI actually do?
We find operational problems worth solving, then build the right mix of AI, automation, or process redesign to fix them — technology second, not first.
Get the info you need
Frequently asked questions.
What we do, what it costs, how we keep AI honest, and the AI terms buyers actually search for — straight answers, no sales fluff.
Getting started
How does the free call work?
30 minutes, no deck, no prep on your side. We’re figuring out together whether there’s a problem here worth your money — if not, we’ll say so.
What happens after the assessment?
You get a written report you own: findings, a prioritized opportunity list, and what to do first. You can act on it yourself, hand it to your IT team, or bring us in — nothing obligates you to the next step.
Pricing & engagement
How much does this cost?
The assessment is a flat $1,500–$2,500 depending on company size, agreed up front. Ongoing work runs $2,500–$12,000/month depending on how much you lean on us, or a fixed project fee of $7,500–$25,000.
Is there a long-term contract?
No. Every engagement is month to month with 30 days’ notice — no annual lock-in.
Can I get a discount?
Yes — 30% off any ongoing model for six months, in exchange for letting us publish the engagement as a case study. You see anything we write before it’s live, and financials are never published without your sign-off.
Trust & AI safety
Will this hallucinate or make things up?
The risk is substantially reduced, not eliminated. Answers are grounded in your actual ERP and inventory data, guardrails keep tools inside an approved scope, and human review catches anything that needs a judgment call.
What stops the AI from doing something it shouldn’t?
Guardrails — rules built into every tool that keep it inside an approved scope, plus human-in-the-loop review on anything that needs a judgment call before it hits your ERP.
Is my data secure?
Read-only access, permissions, sensitive-data controls, validation and auditability are part of the design, not an afterthought.
How it works
What’s RAG, and why does it matter?
Retrieval-augmented generation — the AI looks up your real data before answering, instead of guessing. It’s why results are accurate instead of just plausible-sounding.
Do I need clean data first?
No — the assessment tells you what you’re actually working with, AI-in-use-today included, so you’re not guessing about your own systems either.
Results
What results have you actually delivered?
81% labor cost reduction on AI-driven intake, 300,000 parts backfilled at 98% accuracy in 9 days, 75% lower cost on bulk receiving — real numbers from real prior operations, detailed in the case studies.
AI terms, plain English — trust & safety
Hallucination
When an AI model confidently states something false or made-up, because it’s predicting plausible text, not verifying facts.
Model Drift
When an AI model’s accuracy degrades over time because real-world data has changed since it was built or last checked.
Guardrails
Rules built into an AI system that keep it from taking actions outside an approved scope.
Human-in-the-Loop (HITL)
A workflow where a person reviews or approves an AI’s output before it’s acted on, rather than the AI running fully unsupervised.
Grounding
Connecting an AI’s answers to a verified, real source of truth so it can’t drift into invented information.
Confidence Score
A number the AI attaches to its own answer showing how sure it is — low scores flag results for human review.
Human Oversight / Review Queue
A checkpoint where flagged AI outputs wait for a person’s approval before proceeding.
Explainability (XAI)
The degree to which a human can understand why an AI system produced a specific output or decision.
Algorithmic Bias
When an AI system produces skewed or unfair results because of patterns in the data it was trained on.
Alignment
Making sure an AI system’s actions and outputs match what the business actually wants and values, not just what’s technically possible.
AI Governance
The policies and oversight that decide what an AI system is allowed to do, who’s accountable for it, and how it’s monitored — the umbrella guardrails and audit trails sit under.
Shadow AI
AI tools employees use on their own, outside any approved or monitored system — often invisible to leadership until something goes wrong.
AI terms, plain English — how the tech works
Agent Harness
The software layer that runs an AI agent — controls which tools it can use, what data it can touch, and when it stops.
Retrieval-Augmented Generation (RAG)
An AI technique where the model looks up real data before answering, instead of relying only on what it was trained on.
Agentic AI
AI that can take multi-step actions toward a goal on its own — not just answer a question, but complete a task.
Fine-Tuning
Additional training on a general AI model using a company’s own data, so it performs better on that company’s specific task.
Prompt Engineering
The practice of carefully wording instructions given to an AI model to get reliable, accurate output.
Orchestration
Coordinating multiple AI steps or tools in the right order to complete a full workflow, not just one task.
Tool Use / Function Calling
When an AI model reaches outside its own training data mid-task — calling a live database, web search, or API — and uses the real result to finish or check its answer.
Large Language Model (LLM)
The type of AI model trained on huge amounts of text and data to understand and generate language.
Multimodal AI
AI that can process more than one type of input at once — for example, a photo and text together, not just text alone.
Embeddings
A way of converting text, images, or data into numbers that capture their meaning, so an AI can compare and match similar items.
Vector Database
A specialized database that stores embeddings, allowing fast similarity search.
Context Window
The amount of information an AI model can “see” and consider at once when generating a response.
Inference
One run of an AI model — putting information in and getting a result out.
Chain-of-Thought (CoT)
A method where an AI works through a problem in visible, step-by-step reasoning before giving a final answer.
Zero-Shot / Few-Shot Learning
An AI’s ability to handle a new task with little or no prior specific training.
Edge Case
An unusual or rare situation that falls outside an AI system’s normal, well-tested scenarios.
Synthetic Data
Artificially generated data used to train or test an AI system when real data is limited or sensitive.
Sandbox / Sandboxing
A safe, isolated environment where an AI system can be tested without affecting real, live business operations.
Latency
The delay between asking an AI system something and getting a response — lower latency means faster results.
OCR (Optical Character Recognition)
Software that reads text from a photo or scanned document and turns it into usable data — the first step before AI can act on it.
Structured Data Extraction
Pulling specific facts — part numbers, prices, specs — out of a photo, PDF or webpage and putting them into clean, usable fields.
Anomaly Detection
Automatically spotting an item, count or record that doesn’t match the expected pattern — a wrong bin, a missing unit, a number that’s off.
Data Pipeline / ETL
The automated path data takes from a source — a photo, a website, a spreadsheet — through cleanup and matching to where it’s actually used.
API Integration
Connecting two systems, like an ERP and a marketplace, so they can trade data directly without manual re-entry.
AI terms, plain English — buying decision
Automation Workflow
A defined sequence of steps where an AI (or AI plus a human) completes a business process end-to-end, replacing manual steps.
Benchmark
A standardized test used to compare AI systems’ performance against each other or against a fixed standard.
ROI (AI-specific framing)
Return on investment calculated specifically for an AI project — labor hours saved, error reduction, speed gained, translated into dollars.
Start with a conversation
What problem is costing you more than it should?
Let’s spend 30 minutes looking for the first problem worth solving.