For output that has to survive more than one demo
Generative AI Development
A generative AI demo is easy. A generation feature that stays reliable across a year of real usage is not, because quality drifts the moment nobody is scoring it. This practice builds the pipeline, the evaluation and the guardrails together, whether the output is customer-facing text, an internal drafting tool, or a new modality.
How a generation pipeline earns its guardrails
Content scope, an automatic quality score and the checkpoints that catch a bad output.
90 secSample frame · video pending01Before any pipeline
The generation decision worth making explicit
A generative feature that looks fine in a demo can still be the wrong build if nobody has agreed what good output means. Three checks settle that before a pipeline gets designed.
Does the output need verifying, or just be useful?
A drafting tool tolerates occasional weak output. A customer-facing feature does not, and the guardrail budget follows that difference.
Can quality be scored without a human every time?
Automatic evaluation needs a defensible definition of good output. Without one, every release becomes a manual quality argument.
What happens when the model gets it wrong?
A fallback path, a confidence threshold or a human checkpoint must exist before launch, not get discovered after a bad output ships.
02Agreed before any pipeline
The ledger that keeps generation honest
Every generative engagement is scoped before the pipeline starts: what content you supply, what gets built around it, what you receive, and what stays yours no matter how the build proceeds.
- Your side
You bring
- The content type and the quality bar it must clear
- Examples of good and unacceptable output
- Any brand or compliance constraints on generated content
- Our side
We build
- A generation pipeline tuned to your content type
- Evaluation criteria that score output quality automatically
- Guardrails that catch unsafe or off-brand output
- Your side
You receive
- A generation feature with a measured quality baseline
- An evaluation suite that scores new output automatically
- Guardrails documented for your content and risk owners
Never leaves you
- Brand voice and content approval standards
- The underlying content and training data
- Final say on what output ships to users
Where the line sitsWe build the pipeline and its guardrails. What counts as acceptable output is a call only you can make.
03Three drivers, not a rate card
What actually drives generative AI cost
Generative work is priced against the assessment, not a template, because these three factors change the effort involved by multiples.
Content type and volume
A narrow, high-volume content type is cheaper to tune than a broad one covering many formats and audiences.
Evaluation and guardrail depth
A basic content filter is fast to add. A full evaluation suite with human-reviewed scoring takes materially longer.
Number of modalities in scope
Text-only generation is the simplest case. Adding image or multimodal output adds separate evaluation and cost per modality.
04Evidence and proof
Generated output that stayed inside the guardrails, month after month.
Generative AI proof is a quality score that held after launch, not a single impressive sample. These previews stand in while written client approval for each record is collected.
Explore case studiesA product-description generator cut drafting time without losing brand voice.
Tern's catalogue team wrote every listing by hand. A generation pipeline with brand-voice scoring now drafts listings a human still approves.
- Listings drafted automatically
- Majority, human-approved
- Brand-voice score at launch
- Measured against a written rubric
A course-summary generator shipped with an evaluation rubric instructors trust.
Pillar Learning wanted automatic lesson summaries without embarrassing errors. A scored evaluation suite let instructors see exactly why an output passed or failed.
- Summaries scored automatically
- Against an instructor-defined rubric
- Flagged for human review
- A defined, monitored share
The pipeline drafts. Our editors still decide. That boundary was built into the tool, not left to a policy nobody reads.
Sample · pending client approval
05Read this first
When generative AI is the wrong starting point
Building a generation pipeline for a task that needs action, not content, would cost more than it earns us in trust. Two alternative starting points are marked before any pitch.
- AI Agent Development
The feature needs to act, not generate text
Content generation is not the same job as taking actions inside a workflow with defined permissions.
- NLP Development
The task is classic language processing, not generation
Classification, extraction and structured NLP tasks often need a different, lighter-weight approach than a generative pipeline.
- AI Consulting
The use case itself is still unproven
Rank and test it first, so the pipeline gets built around a case that has already earned its build.
| Shape | Best when | You direct | We carry |
|---|---|---|---|
| Scoped build | The content type and quality bar are settled | Priorities and sign-off | Pipeline, evaluation, guardrails |
| Delivery pod | Output quality evolves with real usage | Product decisions each cycle | Engineering, evaluation, releases |
| Team extension | Your leads already run generative work well | Architecture and standards | Named engineers and continuity |
06Straight answers
Asked before a generation pipeline gets scoped.
01How is generative AI different from an AI agent?
Generative AI produces content: text, drafts or images a person or system then uses. An agent takes actions inside a workflow with defined permissions. Many products use both, but they are engineered as separate concerns with separate guardrails.
02Do you fine-tune models or use them as-is?
Whichever the case justifies. Prompting a hosted model is faster and cheaper when it clears the quality bar. Fine-tuning earns its cost when accuracy, tone or cost per call demand it.
03How do you stop the model generating something off-brand?
Through engineered guardrails rather than a style guide nobody enforces. Content filters, confidence thresholds and, where the risk warrants it, a human checkpoint sit inside the pipeline itself.
04Can generated content go straight to customers without review?
Sometimes, once the evaluation suite proves the quality bar holds consistently. Many products start with human review and remove it only where the measured error rate justifies the change.
05What does a generative AI build cost?
Scoped work is priced against content type, evaluation depth and how many modalities are in scope, from the assessment. Ongoing delivery is billed monthly against a scope that moves with usage.
06How do you measure output quality without a human every time?
By turning a written definition of good output into an automatic score, validated against human judgments on a sample. The score then runs on every release without a manual review queue.
07Can you add generation to a product that already ships?
Yes, and it is the more common request. The pipeline is engineered to fit the existing product and release process, rather than treated as a separate experiment running alongside it.