language, reasoning, pattern recognition, generation.
knowledge, context, rules, tools, roles, priorities.
designed around a real task, decision or operating need.
reliable execution in the context that actually matters.
Generic AI can write, read and reason. But it doesn’t know your projects, your price lists, who approves what, or where a piece of work stands today. So your people carry all of that in their heads and type it in, every time.
Each person explains the job from scratch. Results vary with who’s asking.
The schedule, the bid, the year-end review. Only a senior person can do it, so everything queues behind them.
How your company prices a job or checks a file isn’t written down. When someone leaves, it goes too.
Different tools on different accounts. Nobody knows what it costs, how good it is, or what company data went where.
Generic AI talks to your organization. Specific AI fits into it.
A general-purpose model can recognize patterns and reason broadly. Useful organizational work depends on what’s particular to your environment.
What your organization knows that a generic model doesn’t.
What’s happening in this customer, project, transaction or situation.
Rules, standards, escalation paths and decision boundaries.
What outcome matters, what should come first, and what should be optimized.
Specific AI combines what AI can work out from your data, records and patterns with what your people intentionally add: purpose, judgment, standards, exceptions, priorities and the outcomes that matter.
It isn’t a smarter model. It’s the same models, fitted to your work.
| Generic AI | Specific AI | |
|---|---|---|
| What it is | Broad intelligence you talk to | That intelligence fitted to your organization |
| Context | Supplied by the person, every time | Already there: your documents, rules and records |
| Where it sits | Outside the work, in a chat box | Inside a named piece of work, connected to your systems |
| Quality | Depends on who’s asking | Set by the workflow and the role |
| What it produces | An answer | The work moved to its next step |
| When someone leaves | Their know-how leaves too | The method stays with the role |
| Conventional automation | AI-enhanced automation | Specific AI | |
|---|---|---|---|
| How it works | Trigger, rules, action | Trigger, one AI step, action | Situation, your context, your logic, then a decision or action |
| Best for | Inputs and rules that never change | Adding a summary, extraction or draft to a fixed process | Work where context, standards and judgment matter |
| What drives it | Logic written in advance | Logic written in advance, plus one AI task | Your data, rules, roles and the current state of the work |
These work well together. Specific AI does the part the other two can’t do on their own: fitting AI, your data and your organization’s way of working around one job, so it can be done reliably.
The goal isn’t more AI. It’s making the valuable know-how in your organization available and applicable, consistently, wherever the work happens.
Relevant know-how reaches the person or team that needs it.
Context and guidance appear at the point of decision or action.
Good practice and judgment are applied the same way across similar situations.
Fewer avoidable omissions, inconsistencies and variations.
Teams work from common logic, not isolated individual knowledge.
Important know-how doesn’t dilute, drift or disappear over time.
Combine context, precedents, rules and human judgment to support repeatable decisions.
Prepare, validate, compare and flag issues using your company’s standards.
Recognize when a situation should move to another role, authority or way of acting.
Bring the right organizational knowledge to the right role at the right moment.
Apply evidence, rules, documentation and approval logic consistently.
Coordinate data, tools, roles and actions toward a defined real-world outcome.
Across project management, billing, contracts, quality and site operations, connected to their project management system.
For accounting firms and finance teams. Every finding has a source, an owner, evidence and a review.
Refund statements, supporting schedules and declarations prepared from export invoices, shipping documents and purchase records, with mismatches flagged for review.
Compliance, sales and go-to-market, customer operations, knowledge work and decision workflows. If work comes back often, follows your standards, has an owner and a visible result, we can build it.
All three sites are open. You’ll see the review step, the confirmed values and the documents that come out.
Turn an agreement into a provisional schedule, a payment sheet into invoices, or a scope of work into a quality checklist workbook.
Run a reconciliation, a financial statement review or a tax return workflow.
Prepare a refund statement or reconcile purchases against tax records.
The way in isn’t a broad AI transformation. It’s one real workflow where generic AI isn’t enough.
Start with one workflow. We build it around your real work, you try it, and you only pay if it works.
We build that first workflow from your own material, with the person who does the job best. You check it and say yes. Nobody on your side starts from a blank page.
After the first workflow, an engagement has up to three parts, each agreed in writing: design and implementation (one-time), a Bleno subscription (recurring), and usage, support and expansion (as agreed).
Bleno is the product layer for Specific AI: one platform used to build, deploy and operate Specific AI workflows across finance, compliance, construction, sales and more. Each implementation brings together models, data, tools, rules and your organization’s own operating context.
Specific AI is a YE Stack category. Bleno is a YE Stack product. TGH Tech is an implementation partner: we design, build and support the workflows inside your organization.
Specific AI spreads faster when the people who know the work best help shape it.
Put a real workflow in early, and shape Specific AI for your industry.
Bring workflows, clients or the systems they run on. The unit of collaboration is a real organizational problem.
Definitions, use cases, build logs, research and critique, in the open.