AI solutions and intelligent automation
Practical automation with a person approving what matters
AI consulting for Denver SMBs — document processing, workflow automation, and forecasting layered on the ERP and accounting systems you already run
Table of Contents
Your team spends hours every week re-keying invoices, routing forms, and assembling the same reports. We use artificial intelligence (AI) to take that work off their plate — layered over the systems you already own, with an audit trail you can show your accountant and a person approving anything that matters.
The problem we solve
Every software vendor is now an AI vendor, and the demos all look the same. What an owner actually needs to know is simpler: where does this pay for itself, what happens to our data, and who catches the mistakes. Meanwhile the real cost keeps accruing quietly — an office manager keying invoices every Friday, a paralegal re-typing intake forms, exceptions nobody sees until month-end.
Our answer is to treat AI as an overlay, not a replacement. The foundation stays deterministic and auditable — scripts, integrations, and systems of record you can inspect. The AI layer reads the documents, drafts the summaries, and flags the exceptions, and it does so faster than any person re-keying data by hand.
What we do
- AI readiness and guardrails — assess where AI actually pays across your current systems and scripts, check whether your data is ready, and set the governance: what the AI may touch, what it may not, and how every action gets logged.
- Plain-English workflow automation — turn repetitive back-office steps into prompt-driven workflows running over deterministic, scripted foundations, so non-technical staff can trigger and adjust them without calling a developer.
- Document and data processing — optical character recognition (OCR) and language-model extraction for invoices, contracts, intake forms, and email, producing structured output that feeds your Enterprise Resource Planning (ERP) and accounting systems instead of a copy-paste queue.
- Decision support and forecasting — anomaly detection, cash-flow forecasting, and automated summaries layered over your data warehouse, so exceptions surface during the month rather than at close.
How your data is handled
Three commitments shape every AI engagement we run:
- Your data stays in your tenant wherever possible. We favor architectures where documents and extractions live in your own cloud environment, not a third party’s.
- No training on your data. We configure providers under their business terms so your inputs and outputs aren’t used to train their models. The major vendors publish these commitments — see Anthropic’s commercial terms and Microsoft’s data, privacy, and security documentation for Azure OpenAI — and we verify the exact terms for each provider as part of the engagement.
- A human stays in the loop. Compliance-critical steps — payments, filings, anything an auditor or regulator will ask about — route to a person for approval, and every automated action is logged.
Who it’s for
- An accounting or law firm where staff key vendor invoices and client intake documents by hand, and billable people are doing non-billable data entry.
- A healthcare or dental clinic routing patient intake forms, where any automation has to respect the safeguards the Health Insurance Portability and Accountability Act (HIPAA) requires for protected health information.
- A light-manufacturing or distribution business matching purchase orders, receipts, and invoices three ways, where mismatches surface weeks late and hold up payments.
If you’ve caught yourself saying “someone spends every Friday re-keying invoices” or “we found that error two weeks after close” — that’s the workflow we’d start with.
How we work
- Discovery. A free conversation about where the hours actually go and which workflows are worth automating first.
- Readiness assessment. In 2–3 weeks we map candidate workflows, test whether your data supports them, and design the guardrails. You get a prioritized list with the expected payback for each.
- Pilot. We put the first workflow into production — typically 4–8 weeks — with a human reviewing outputs from day one, so trust is earned on real work.
- Rollout and transfer. We extend what works, train your staff to run and adjust the workflows, and hand over the runbooks.
What to expect
We measure these projects in hours returned to staff, exception rates caught earlier, and whether people actually use the thing. A readiness assessment runs 2–3 weeks; a first production workflow typically lands in 4–8 weeks; a broader program runs 2–4 months in phases. No moonshots — a pilot small enough to prove itself before you spend more.
How we run our own practice on this
We use this playbook on ourselves before we recommend it to anyone. The proposals, content, code, and internal reporting behind this practice run on the same pattern we sell: deterministic scripts underneath, AI drafting and reviewing on top, a person approving what ships. The tools we build and work with are documented publicly, so you can see the working model rather than take our word for it.
Tools and platforms
Anthropic Claude, Azure OpenAI Service, and OpenAI models under business terms; Azure AI Document Intelligence and Amazon Textract for OCR; Python and shell-based orchestration; outputs feeding QuickBooks, NetSuite, and the ERP systems we support.
Related
- [[Software development]] — when the automation needs an application around it.
- [[Data and BI]] — the warehouse that forecasting and anomaly detection sit on.
Ready to talk?
Tell us which repetitive workflow costs your team the most hours. We’ll start with a free discovery call and tell you honestly whether AI is the right fix — or whether a simpler script would do.
📧 Email: info@bashconsultants.com
📞 Phone: (720) 352-4641
🔗 LinkedIn: Connect with Amr
🌐 Website: bashconsultants.com