AI is the new tool. The experience is what makes it useful.
There's a big difference between building AI and applying AI inside a real business. We've spent years inside the work we now build for — financial markets, investments, accounting, business administration, fund operations, automation, and process improvement across many kinds of small and mid-sized businesses. AI is a tool. Experience is what makes the tool useful.
The numbers are real, when the work is done right
AI is the most over-hyped and under-applied technology in small business today. Sprinkled on, it's a buzzword. Applied properly — to specific operational tasks, with measurement and feedback — the gains are well-documented across the industry. Independent research consistently shows measurable, repeatable improvements where AI is deployed thoughtfully:
30–50% shorter close cycle
Receipt/expense processing alone sees 70–80% time reduction. Sourced from Big 4 and AICPA industry studies on AI in accounting.
30–45% productivity gain
Support, intake, and routine inquiries. McKinsey and Bain studies of AI-augmented customer operations across multiple industries.
60–80% time reduction
Contract analysis, lease review, due diligence. Stanford Law and legal-AI industry research on document-heavy workflows.
20–45% faster delivery
Code generation, testing, refactoring. GitHub research and McKinsey reports on AI-augmented engineering teams.
10–15% revenue lift
Plus 10–15% cost reduction. McKinsey and BCG studies of generative AI in customer-facing functions.
40–60% search-time reduction
Finding what you already know inside contracts, SOPs, and operating documents. Cross-industry knowledge-management studies.
These ranges reflect industry research on AI applied to specific operational tasks. Actual results depend on the engagement — the work, the data, the level of integration, and whether humans are kept in the loop where they should be. We measure outcomes on every engagement and tell you, before we start, what good would look like.
The 45-minute task that should take 10.
Every business owner knows this one. A task that should take ten minutes — reconciling a transaction, sending a follow-up, filing a document, chasing an invoice — somehow eats forty-five. Context-switching, missing information, errors found late, the same data re-typed into three different systems.
When the work gets automated properly — sometimes with AI, sometimes with plain software, often a mix — it becomes a two-minute, no-error task that just gets handled in the background. You stop thinking about it. That's the actual goal. Not "more AI." Less work that requires your attention.
What "AI" actually means right now
When people say AI today, they usually mean two things working together: large language models (the things that read and write like ChatGPT) and agents (software that uses those models to actually do tasks — read a document, fill out a form, send a follow-up email, categorize a transaction).
What's new isn't the math — neural networks have existed for decades. What's new is that these systems became good enough, fast enough, and cheap enough that they can do real operational work inside a small business, not just chat in a sandbox.
What's still true: AI doesn't replace judgment, doesn't always tell the truth, and works best when a human is in the loop for the decisions that matter. The companies getting value from it aren't deploying it everywhere — they're deploying it carefully, in the places where it changes the answer.
Plain-language definitions
- LLM
- A model that reads and writes language. Good at summarizing, drafting, classifying, and extracting information.
- Agent
- Software that uses one or more models to perform a task — like a small program with a brain.
- Embedding
- How AI turns text into searchable numbers. The reason "search by meaning" works now.
- Fine-tune
- Teaching a general model your specific vocabulary, data, and rules.
- RAG
- "Retrieval-Augmented Generation" — letting the model look up your documents before answering.
By function, with concrete examples
Skip the buzzwords. Below is what AI actually does inside the parts of a business that most operators spend their week on.
Bookkeeping & accounting
AI quietly handles the categorization, matching, and document work that used to fill bookkeeper hours.
- Read a receipt photo and pull out vendor, date, amount, and category automatically
- Categorize incoming transactions based on your history and rules — not rigid keyword lists
- Match invoices to payments and surface mismatches
- Draft month-end close adjustments and flag anomalies before they hit reports
- Generate 1099 prep and tax-document organization at year-end
Client & sales workflows
Lead intake, follow-up, and proposal work that used to fall through the cracks.
- Read inbound emails, extract who/what/budget/timeline, and create a CRM record
- Draft personalized follow-up emails based on context — not template spam
- Generate estimates and proposals from a short brief, using your standard pricing
- Summarize a long client thread into the three things you need to decide
- Detect deals stalling and surface what to do next
Documents & knowledge
Everything-in-a-pile becomes everything-findable.
- OCR and tag every contract, statement, certificate, and receipt automatically
- Search by meaning, not just keywords ("what's the renewal clause for the lease in Plano")
- Extract key terms — expiration dates, renewal notice periods, payment terms — into a tracker
- Compare two contracts and tell you what changed
- Watch for renewal/expiration deadlines and remind you before they bite
Operations & admin
The "ten little things" that fill an owner's day.
- Read incoming forms/intakes and route them to the right workflow
- Draft routine emails — vendor inquiries, status updates, internal memos
- Summarize meeting recordings into action items and decisions
- Generate SOPs from how you describe a process verbally
- Triage support / tenant / customer messages and flag what needs your eyes
What it looks like in your specific business
Property management
- Tenant messages auto-triaged — maintenance, billing, complaint, leasing — and routed
- Maintenance photos analyzed to draft work orders
- Lease documents parsed into a structured tracker (renewal, escalation, deposit terms)
- Per-property P&L generated automatically from transactions
- Late-rent escalation drafts written and ready for your approval
Federal / DOD contractors
- Solicitations on SAM.gov screened automatically against your capabilities and NAICS
- Past-performance database searched by meaning to pull the right case study for each proposal
- Capability statement variants generated by agency or solicitation
- RFP/RFI sections drafted from your prior work, ready for your editor's pass
- DCAA-style time entries categorized and labor-distribution checks pre-flagged
Multi-entity owners
- Transactions assigned to the right entity automatically based on rules and context
- Cross-entity cash, P&L, and credit-portfolio rollups updated daily
- Inter-company transfers and allocations tracked without spreadsheet juggling
- Compliance calendars (BOI, sales tax, license renewals) maintained per entity
- Document vault searchable across every entity by meaning
Funds & financial firms
- Investor inquiries triaged and draft responses prepared from policy docs
- Capital activity (subscriptions, redemptions, capital calls) processed and reconciled
- Portfolio and exposure reports generated automatically
- NDA and side-letter terms extracted into a tracker
- Research notes summarized, tagged, and made searchable across the whole knowledge base
Independent owners & trades
- Lead inquiries routed and replied to within minutes, even after hours
- Estimates and quotes drafted from short voice notes
- Receipts and mileage captured by photo and filed correctly
- Job photos organized by client and job
- Recurring invoicing and AR follow-up handled in the background
Software vendors & platforms
- Embed AI search, drafting, and decision-support inside your product
- Add AI-powered analytics on top of your existing data
- Build agents for your customers' workflows under your brand
- Fine-tune models on your domain language and customer data
- Integrate AMG's licensed AI components rather than building from scratch
What separates working AI from buzzword AI
We're not selling you AI. We're selling you years of operational experience in financial markets, business operations, business administration, automation, and process improvement — and using AI where it makes that work faster, sharper, and cheaper. There's a difference.
Right model for the job
Not every problem needs the biggest model, and not every problem needs a model at all. We pick the right tool — sometimes that's an LLM, sometimes a classifier, sometimes plain rules.
Humans in the loop where it matters
AI drafts — you approve. AI categorizes — you can override. AI surfaces decisions — you make them. The places we don't put a human in the loop are the places it provably doesn't matter.
Measure outcomes, not features
"We added AI" isn't a result. "Your monthly close dropped from 9 days to 3" is a result. Every engagement has a measurable outcome defined up front.
Your data, your model
For sensitive work, we deploy models that run on your data without it leaving your control. Cloud providers' general-purpose APIs are fine for most things, but we use them deliberately.
Honest about limits
AI hallucinates. AI can be confidently wrong. AI doesn't replace judgment. We tell you where the system might fail before we ship, not after.
Real evaluation, not vibes
Every AI feature we ship has a test set, success criteria, and ongoing monitoring. "It works in the demo" isn't acceptance — measurable accuracy on your real data is.
Have something you'd like to automate?
Send a short description of the work you're doing today by hand. We'll tell you honestly whether AI would change it, what it would look like, and roughly what it would take.