Generative AI Development
Outcomes
Output volume that was previously constrained by people
Personalised content, product descriptions, first-draft documents, variant generation. The constraint moves from production capacity to review capacity, which changes how you staff the function.
Quality that is measured rather than assumed
We build an evaluation set and score output against it. Generative systems fail gradually and invisibly without this, and "it seemed fine in testing" is not a quality process.
Cost you can predict
Token spend modelled before build, instrumented in production, and controlled through model routing — expensive models only on steps that need them. Generative features are where AI bills quietly escalate.
Brand and safety guardrails that hold
Tone, factual constraints, prohibited content, required disclaimers. Enforced structurally rather than requested politely in a prompt.
What we build
Content generation systems grounded in your data and constrained to your voice, with human review workflows where output is customer-facing.
Document generation — proposals, reports, summaries, filings — assembled from structured inputs with the parts that must be exact handled deterministically rather than generated.
Code and technical generation for internal developer tooling, migration assistance, and test generation.
Image and creative generation pipelines where visual volume is the bottleneck, including brand-constrained generation and asset variation.
Structured output systems that produce validated JSON, database records, or API payloads. Considerably harder than free text, because malformed output breaks downstream systems rather than merely reading badly.
Evaluation and review tooling. Scoring harnesses, human review queues, and feedback capture that improves the system rather than disappearing into a spreadsheet.
How it works
Weeks 1–2 — Definition. What good output looks like, in writing, with examples. This sounds obvious and is skipped constantly. Without an agreed definition of quality, the project has no finish line.
Weeks 2–3 — Evaluation set. Real inputs with reference outputs, plus scoring criteria. Built before the system so improvement is measurable rather than asserted.
Weeks 3–6 — Build. Prompt and context architecture, model selection, structural guardrails, integration. Scored continuously against the evaluation set.
Weeks 6–8 — Human review design. Where output is reviewed, by whom, and how corrections feed back. Systems that make review fast get used; systems that make it tedious get bypassed.
Weeks 8–10 — Pilot and tuning. Real usage, failure collection, iteration. Cost instrumentation verified under real load.
Ongoing. Re-evaluation as models change — and they change frequently, sometimes altering output character without warning. Version pinning and regression testing are part of the build, not an afterthought.
Technology
Models: Claude, GPT and Gemini for quality-sensitive generation; open-weight models where volume economics or data residency favour self-hosting. Image generation via the current leading providers, selected per use case.
Structured output: schema-constrained generation with validation, retry and fallback. Never trust generated JSON without validating it.
Evaluation: LLM-as-judge for subjective quality, deterministic checks for anything verifiable, human review for the sample that matters.
Infrastructure: your cloud, with caching, batching and model routing to control cost at volume.
Where this applies
Strongest where content volume is a genuine bottleneck and quality tolerance allows human review rather than requiring perfection unattended.
Weakest where every output must be exactly correct with no review, or where volume is too low to justify the build. A skilled person with a good template often beats a generative system at low volume.
How we scope and price
Fixed scope per system, quoted after a discovery call. Cost is driven by output complexity, how tightly quality must be controlled, integration depth, and whether structured output is required. Ongoing inference cost is modelled separately and stated explicitly — a build quote that ignores run cost is an incomplete quote.
Frequently asked questions
Grounding output in your own data, structural constraints rather than polite instructions, validation of anything checkable, and human review where the cost of an error justifies it. Risk is reduced and measured, not eliminated — any vendor claiming elimination is overselling.
It will if the system is built by prompt alone. Voice comes from grounding in your existing material, explicit style constraints, and evaluation that scores tone rather than only correctness. This is a large part of the work.
Generally you, though it depends on the model provider's terms and your jurisdiction — and for images, on training-data litigation that is still unresolved. We flag the specific terms of whichever provider we recommend so you can make an informed choice.
Model routing by step, caching, batching where latency allows, and instrumentation that attributes spend per feature. Most generative applications run far more cheaply than their first implementation, because the first implementation sends everything to the most expensive model.
Output can shift, sometimes noticeably. We pin versions where the provider supports it, keep the evaluation set as a regression suite, and re-run it on every model change. This is routine maintenance, not an emergency.
Yes — that is usually the difference between output you can use and output you cannot. Existing material is the strongest available signal for voice.
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