Generative AI Development
Generative AI development is the building of applications where a model produces new content — text, images, code, structured documents or synthetic data — as the core of the product rather than a feature bolted on. We design and ship these systems end to end: prompt and context architecture, evaluation, guardrails, cost control, and the interface people actually use.
The problem
Where the time actually goes
Everything arrives as a first draft that takes as long to fix as to write
Someone tried a general tool. The output is roughly there, and the remaining fraction — the factual claim that is subtly wrong, the tone that is not quite yours, the detail only an insider would catch — is where the entire time goes. Nothing was saved, and the team has concluded the technology does not work.
Quality is whatever the last person to look at it thought
There is no sample review, no scoring, no record. Output goes out, and the first real signal that something drifted arrives from a customer or a regulator.
The bill tripled and nobody can say which feature did it
One prompt got longer during a fix, one feature became popular, and the invoice is a single line with no attribution. The conversation about whether it is worth it cannot even begin, because nobody can decompose the number.
What a real fix takes
The part everyone skips is writing down what good output actually is, with worked examples, before anything gets built. It feels obvious and it is not: teams that skip it spend eight weeks arguing about tone with no way to settle the argument, because "better" is an opinion until someone has written down the rule it violates. A generative system without a graded example set has no finish line, no way to prove a change was an improvement, and no defence when a model provider updates underneath it and the output character shifts without warning.
Outcomes
What changes
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 rather than simply shrinking it.
Quality that is measured rather than assumed
Output scored against an agreed set. 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.
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
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. A figure that must match a database should be inserted, not written.
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.
Review tooling built for speed
Queues, diffs, one-key accept and a categorised reason on reject. Review is the actual bottleneck once generation works, and a review surface that is tedious gets bypassed, which quietly removes the only quality control the system has.
Version pinning and regression suites
Model providers update models underneath you, sometimes altering output character without notice. The evaluation set doubles as a regression test, run on every model change.
What you receive
Three artifacts
The output specification
What good looks like, written down, with worked examples in both directions — output that passes, output that fails, and the specific rule each one demonstrates. This is the document that ends the tone argument, and it is what makes a reviewer's rejection actionable rather than a matter of taste. It is also the brief you would hand a new writer, which is why it retains value even if the system is retired.
The review record
Not just a queue — a record of what human reviewers actually changed, categorised, so corrections become signal rather than lost edits in a document nobody re-reads. It tells you where the system is weak, in the reviewers' own words, and it is what the next round of improvement is built from.
The cost model, per unit of output
What one generated document, description or asset costs, decomposed by step and model, with the projection at your real volume. Generative features are where AI bills escalate quietly, and this is the artifact that turns "is this worth it" from a debate into arithmetic.
How it works
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, which is routinely where the first implementation's economics turn out to be wrong.
Ongoing
Re-evaluation as models change, and they change frequently.
The phase clients underestimate is the first. Agreeing what good output is — particularly across a marketing team, a legal reviewer and a product owner who each mean something different by "on brand" — regularly takes longer than building the generator, and every week saved there is spent twice over later.
Technology
What we work with
Technologies we work with, used where they help. Model choice is made per workload and revisited rather than fixed — this layer moves fast enough that a stack settled today is partly wrong within a year.
Models: Claude, GPT and Gemini for quality-sensitive generation; open-weight models where volume economics or data residency favour self-hosting.
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
Where this applies
Strongest where content volume is a genuine bottleneck and quality tolerance allows human review rather than requiring perfection unattended — retail and e-commerce catalogue and description work, media and entertainment production, and real estate listing and document generation are the clearest fits.
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 beats a generative system at low volume, comfortably, and we will tell you where that line sits for your numbers.
Why us
One team, not three vendors
The usual way
- A strategy consultant, a development shop and a marketing agency, each scoped separately.
- Months spent getting three vendors to agree, and blame to allocate when something breaks.
- The model ships and adoption becomes somebody else's problem.
With Nexiour
- One team across strategy, engineering and growth. You skip the coordination tax entirely.
- We own the whole outcome — from the first audit to the system that is running.
- Delivery across the UK, UAE, Germany and India, with overlapping hours in four markets.
What it costs
How we scope and price
Fixed scope per system, quoted after a discovery call. Three things drive it.
How tightly quality must be controlled
A system whose output goes to an internal reader is a different build from one whose output is published under your name or filed with a regulator.
Whether output must be structured
Free text is forgiving. Validated JSON or records that write into a downstream system are not, because malformed output breaks things rather than reading badly.
Review workflow depth
Where every item is checked, the review surface is a real product with its own design and integration work rather than a queue.
Ongoing inference cost is modelled separately and stated explicitly. A build quote that ignores run cost is an incomplete quote.
FAQ
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.
When volume is low — a person with a good template wins below a threshold we will calculate with your numbers. When every output must be exactly right and there is nobody to review it. And when what you actually need is retrieval rather than generation: if the requirement is "answer from our documents", that is RAG, and it is a different and usually cheaper build.
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, considerably stronger than a written guideline.
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