Generative AI
Generation With a Review Step.
Content, code and document generation built into real workflows — with the source material, brand constraints and human approval that make the output usable rather than merely fast.
What this covers
- Grounded generation
- Structured output
- Brand and tone constraints
- Document assembly
- Integrates with
- 7 system types
- Built on
- OpenAI · Anthropic
What it is
Generative AI, in plain language
Generative AI is straightforward to demonstrate and difficult to operationalise. The demo produces one good output. Production requires consistent output, on brand, factually grounded, and reviewable at volume.
The engineering is in the constraints: what source material the generation draws on, what structure it must conform to, what it may never claim, and who signs off before anything is published or sent.
Capabilities
What the system does
Grounded generation
Output drawn from your approved source material rather than model recall
Structured output
Schema-conforming results that downstream systems can consume without parsing prose
Brand and tone constraints
Style enforced by system design and validated, not requested in a prompt
Document assembly
Proposals, reports and summaries built from live data and approved templates
Review workflows
Generated drafts routed for approval with the sources shown alongside
Batch generation
High-volume production with per-item validation and failure isolation
How it works
The architecture, not the pitch
Source material and constraints in, structured draft out, human approval before anything leaves the system.
- InputTriggerWebhook · queue · inbox · file drop · schedule
- ReasoningInterpretationUnstructured input becomes structured data
- DecisionRouting callClassified and routed against your criteria
- SystemSystem actionCRM · ERP · ticketing
- ActionResultRecorded with its inputs and reasoning
- Human escalationException branchAnything outside the path goes to a person
The hard parts
What makes generation usable at volume
One good output is a demo. The engineering is in the thousandth one being as safe as the first, and in someone being able to tell.
- 01
Grounding is what makes output checkable
Generation drawn from approved source material can be verified against that material. Generation drawn from model recall cannot be verified at all. That difference is what turns review into a task of seconds rather than minutes.
- 02
Structure is enforced, not requested
Output that a downstream system consumes conforms to a schema and is validated before it is accepted. Asking for JSON in a prompt is a hope; rejecting and repairing non-conforming output is a contract.
- 03
The important constraints are what may never be said
Brand and tone are the easy half. The hard half is the list of claims the system must never make — a price, a guarantee, a clinical or legal statement — enforced by validation rather than by instruction, because an instruction is a preference the model weighs against everything else.
- 04
Review has to scale or it becomes the bottleneck
If a person reads every word, generating faster has not helped anyone. Review surfaces show what changed and which parts were grounded, so attention lands on the passages that actually need a human.
- 05
Quality is subjective here, so evaluation is comparative
There is no single correct output, which makes accuracy scoring the wrong instrument. Generation is scored against held-out examples and by pairwise comparison between versions — so a prompt change can be shown to be an improvement rather than assumed to be one.
- 06
Prompts and templates are versioned artefacts
A generation system’s behaviour lives in its prompts and templates as much as in its code. Both are versioned and outputs record which version produced them, because otherwise a regression cannot be traced back to the change that caused it.
Use cases
From trigger to business outcome
Every row reads the same way, because every system does: something happens, the model interprets it, an action lands in a real system, and the business result follows.
| Use case | Trigger | AI reasoning | Action | Result |
|---|---|---|---|---|
| Proposal drafting | An opportunity reaches proposal stage | Retrieves scope notes, prior comparable proposals and current pricing structure | Assembles a draft in your template for review | Proposals stop being written from a blank page |
| Report generation | A reporting period closes | Pulls the period’s data and identifies what actually changed | Produces a narrative report with the figures cited | Reporting time goes to interpretation, not assembly |
| Product content at scale | New items enter the catalogue | Generates descriptions from specifications within brand and compliance constraints | Writes to the CMS as drafts for approval | Catalogue coverage stops depending on copywriter capacity |
| Meeting and call summaries | A call or meeting ends | Identifies decisions, owners and commitments from the transcript | Writes the summary to the CRM and creates the follow-up tasks | Follow-ups stop depending on whoever took notes |
Proposal drafting
- Trigger
- An opportunity reaches proposal stage
- AI reasoning
- Retrieves scope notes, prior comparable proposals and current pricing structure
- Action
- Assembles a draft in your template for review
- Result
- Proposals stop being written from a blank page
Report generation
- Trigger
- A reporting period closes
- AI reasoning
- Pulls the period’s data and identifies what actually changed
- Action
- Produces a narrative report with the figures cited
- Result
- Reporting time goes to interpretation, not assembly
Product content at scale
- Trigger
- New items enter the catalogue
- AI reasoning
- Generates descriptions from specifications within brand and compliance constraints
- Action
- Writes to the CMS as drafts for approval
- Result
- Catalogue coverage stops depending on copywriter capacity
Meeting and call summaries
- Trigger
- A call or meeting ends
- AI reasoning
- Identifies decisions, owners and commitments from the transcript
- Action
- Writes the summary to the CRM and creates the follow-up tasks
- Result
- Follow-ups stop depending on whoever took notes
Integrations
Built around the systems you already run
- CMS platforms
- Google Workspace
- Microsoft 365
- Salesforce
- HubSpot
- DAM systems
- Custom APIs
Technology
What this is built with
- OpenAI
- Anthropic
- Gemini
- Python
- TypeScript
- PostgreSQL
- Redis
How we work
Five phases, each with a decision point
- InputDiscover
- ReasoningArchitect
- DecisionPrototype
- SystemProduction
- ActionOptimize
FAQ
Questions we are actually asked
How do you keep it on brand?
Structural constraints, retrieval from approved material, and validation on the output — not a long prompt asking politely. Anything that fails validation is regenerated or flagged rather than published.
Will it invent facts?
Ungrounded generation will. We ground it in your source material and validate factual fields against systems of record, so invented content fails a check rather than reaching a customer.
Does a person still review everything?
At the start, yes. Which categories can later publish without review is a decision you make from the observed error rate, not one we make up front.
Who owns the output?
You do. Provider terms vary and we confirm them in writing for the specific models used before anything goes into production.
Can it match our existing writing?
It can be constrained toward it using your existing material as reference. It will be close and consistent; it will not be indistinguishable from a specific writer.
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