AI DEVELOPMENT & AUTOMATION / APPLICATIONS
Applications powered by large language models—copilots, generation tools, classification and extraction systems, multimodal workflows—engineered with evaluation harnesses, cost budgets and fallback behaviour so they hold up outside the demo.
Built for product and engineering teams adding AI features to existing software, startups building AI-native products, content and marketing operations at scale, and businesses with document-heavy generation, summarisation or extraction workloads.
Generative AI and LLM development services build applications powered by large language models: copilots and assistants, content and document generation, summarisation, classification and extraction, and multimodal workflows across text, images, audio and documents. The engineering work is not prompt writing—it is model selection and routing, structured outputs, evaluation harnesses, cost and latency budgets, caching, fallback behaviour and guardrails. It differs from agent development because the system produces output for a person rather than autonomously completing a multi-step process.
THE QUESTION THIS PAGE ANSWERS
Agents, chatbots, RAG and LLM applications overlap technically, so we split them by the decision you are actually making. Pick the question that matches yours.
YOU ARE ASKING
Can you build an application powered by leading LLMs?
This page is about LLM application engineering: model integration and routing, generation and extraction features, multimodal workflows, evaluation, and the cost and latency work that decides whether a feature is viable at scale. If the system needs to take actions in your systems, see AI agents; if answers must come from your documents, start with RAG.
AI & NEURAL EXPERIENCE DESIGN
Select any plane to see what belongs there and what breaks without it. Prototypes usually have the top two layers and none of the rest, which is precisely why they collapse under real traffic.
LLM application architecture
LAYER 01 / UXP
How people work with a probabilistic system
Interfaces designed around the reality that output varies: streaming so waiting feels productive, easy regeneration, editable results, visible uncertainty, and undo. Generative features fail on interaction design at least as often as on model quality.
CONTEXT-AWARE ADAPTIVE STRATEGY
A copilot fails on latency and trust. A generation pipeline fails on quality drift and cost. An extraction system fails on edge cases nobody tested. Select the closest situation.
CONTEXT / In-product copilot
Users want help inside the product, not a separate chat window.
Contextual assistance embedded in the existing interface: streaming responses so it feels immediate, awareness of what the user is currently working on, editable output, and graceful degradation to the normal interface when the model is slow or unavailable.
KEY ADVANTAGES
Most AI work stalls between a convincing demo and a system the business can depend on. These are the advantages that decide which side of that line a project lands on.
We build the measurement before adding capability. Without a fixed evaluation set, every prompt change is a guess and regressions are found by customers rather than by tests—which is the single most common reason AI features degrade after launch.
Classification and formatting go to small fast models; genuine reasoning goes to capable ones. Task-based routing routinely reduces cost substantially compared with sending every request to the largest available model.
Prompts, evaluation sets and business logic live in your codebase behind a provider abstraction. Adding or switching a model is a configuration change, which matters in a market where capability and pricing shift every few months.
Where a result feeds another system, we constrain output to a schema and validate it rather than parsing prose and hoping. This eliminates an entire category of intermittent production failures.
Streaming, caching, prompt compression, parallel calls and speculative prefetch, because a feature that is right but slow gets abandoned. Perceived responsiveness is part of whether the feature succeeds.
Most teams asking for a fine-tuned model need better retrieval or better prompting instead. We will tell you when fine-tuning genuinely earns its ongoing cost and complexity, and when it does not.
Provider outages, rate limits, timeouts and refusals all have defined behaviour: fall back to another model, degrade to a simpler feature, or fail clearly. Silent failure and infinite spinners are design decisions nobody made on purpose.
AI-generated content is marked where a reader could reasonably mistake it for human-authored work, in line with EU AI Act transparency duties applying from August 2026 and with the disclosure norms most platforms now expect.
KINETIC & SPATIAL MICRO-INTERACTIONS
Play the sequence or select any step. Most of these stages are invisible in a prototype and are exactly what makes the difference at a thousand requests a day.
LIVE SEQUENCE / WORKED EXAMPLE
Worked example: an ecommerce team generates product descriptions for 400 new SKUs, with brand rules and factual accuracy that must hold.
STEP 01 / Input assembly
Structured product attributes, category conventions, brand voice rules and three approved examples from the same category are assembled deliberately. Nothing irrelevant is included, because a longer prompt is both a slower and more expensive prompt.
2026 AI BRIEFING
Each trend links to a primary or authoritative source, and to a full briefing page where the evidence, the commercial implication and our exact response are written out.
TREND SIGNAL / ARCHITECTURE
Capable models from several vendors now cluster closely on most business tasks. The engineering advantage has moved from picking a model to routing between them.
WHAT WE BUILD
Eight capabilities behind an LLM feature that holds up in production. Most engagements start with an architecture and evaluation review, because that is usually where an existing prototype is weakest.
The layers a generative feature needs—context assembly, routing, evaluation, caching, fallback and safety—designed for the specific workload rather than assembled from whichever tutorial was open at the time.
AI assistance embedded in software people already use, with streaming responses, awareness of the current task, editable output and graceful degradation when the model is slow or unavailable.
Templated, constrained generation at volume with automated quality scoring, brand and factual guardrails, exception review queues and cost tracked per generated item.
Schema-constrained structured output from messy documents, emails and forms, with per-field confidence scoring, validation against your records and an exception queue for low-confidence cases.
Documents, images, scans, screenshots and audio as first-class inputs—so a photographed invoice or a voice note becomes usable data rather than something a person retypes.
A provider abstraction with task-based routing and fallback across Anthropic, OpenAI, Google and open-weight models, benchmarked on your evaluation set rather than on public leaderboards.
Versioned prompts, evaluation datasets built from real inputs, automated scoring, regression gates before release, and re-runs after provider model updates.
Caching, prompt compression, batching, streaming, token ceilings, spend alerting and defined behaviour for every timeout, rate limit and outage.
USE CASES & SEARCH DEMAND
Fifteen researched searches that lead to this page, and the decision behind each. Filter by cluster to see how a product lead, an engineer and a content operations manager arrive at the same discipline.
Showing 15 of 15 researched buyer searchesFull demand map
Choosing a partner to build a production LLM application.
Needing engineering around a model rather than API access alone.
Comparing partners on production engineering capability.
Adding contextual AI assistance inside existing software.
Extending a working product without rebuilding it.
A demo exists and falls apart under real usage.
Integrating one or more model providers properly.
Cutting cost and surviving provider outages.
Processing documents, images or audio as application input.
An AI feature works but the monthly bill is unsustainable.
Modelling unit economics before committing to a feature.
Looking for caching, routing and compression before cutting the feature.
Needing to prove quality and catch regressions before release.
Choosing the right technique before spending on the wrong one.
Producing volume without publishing output nobody checked.
TECHNOLOGY & INTEGRATION
We do not lead with a vendor name. We choose per workload on capability, latency, cost, data residency and exit risk—then keep the option to switch open.
Selected per task on capability, latency, cost and residency, behind an abstraction that keeps switching cheap.
The product layer where generative features are actually built and shipped.
Prompts treated as versioned artefacts with tests, not as string literals scattered through a codebase.
The harness that makes quality measurable and regressions visible before release.
The engineering that decides whether a feature is viable at a thousand requests a day.
Controlling what the system may produce, and recording how each output was made.
DELIVERY SEQUENCE
The roadmap is sequenced by dependency and expected value. We would rather put one narrow workflow live and measured than demo six that never leave the sandbox.
What the feature must produce, how good is good enough, what it may cost per request and how failure should behave. Features that cannot answer the second and third questions do not proceed to build.
A fixed set of real inputs with expected outputs, including adversarial and must-refuse cases. Built first, so every subsequent decision is measured rather than argued about.
Context assembly, model routing policy, caching strategy, fallback behaviour and a modelled cost per request, benchmarked across two or three providers on your evaluation set.
The feature built with schema-constrained output, validation, streaming, error handling and instrumentation for cost and latency from the first commit rather than added later.
Rate limits, timeouts, retries with ceilings, provider fallback, prompt injection tests and degradation paths verified under realistic concurrency rather than single-user testing.
Controlled launch with quality, cost and latency instrumented, weekly review of failures added to the evaluation set, and re-runs after provider model updates that can shift behaviour unannounced.
MEASUREMENT CONTRACT
Generative features are easy to demo and hard to sustain. These measures are instrumented from the first commit, because a feature whose quality and cost are invisible is a feature that will be switched off during the next budget review.
Score on the fixed evaluation set, tracked as a trend so quality changes are visible before customers find them.
Fully loaded cost per completed user outcome, with the share of requests served by lower-cost models.
Ninety-fifth percentile response time, because the slowest experiences determine whether people keep using it.
Proportion of generated output published without correction, the most honest measure of real quality.
READINESS, PRIVACY, SECURITY & HUMAN OVERSIGHT
Generative systems publish in your name and at your volume. These six controls cover output quality, security, cost and the provenance obligations arriving with the EU AI Act transparency duties in August 2026.
Defined categories the system must not produce, enforced by output filtering as well as prompting, with refusal cases tested as part of the evaluation set before every release.
User input and retrieved content are structurally separated from system instructions and treated as untrusted data, with injection cases in the pre-release evaluation set.
Model, prompt version, timestamp, reviewer and review outcome attached to every produced asset—so any output can be traced, reproduced or recalled, and AI content marked where required.
Automated quality gates pass the clean majority and route borderline output to a reviewer with the failing check highlighted, so review effort concentrates where it changes the outcome.
Token ceilings per request, retry limits, concurrency caps and spend alerting, so a loop or a traffic spike becomes an alert rather than an end-of-month discovery.
Model access configured so your data and your users' inputs are not used for provider training, with in-region or self-hosted deployment available where residency requires it.
GOOGLE SEARCH + AI FEATURES
Everything we ship for you is built the way we built this page: fast, crawlable, factually grounded and structured so an answer engine can quote it correctly.
Everything meaningful here is server-rendered text rather than content behind interaction. Google is explicit that keeping important content in text and allowing crawling underpin AI Overviews and AI Mode as well as classic search.
Service, breadcrumb, FAQ and item-list markup describing exactly what is visible. Markup that overstates the page is a spam-policy issue rather than an optimisation.
We build generation systems with quality gates and human review precisely because Google's spam policies target scaled content produced without regard for value. Volume without a quality gate is a liability.
Every statistic on this page links to a primary source. Unverifiable claims are what answer engines decline to repeat, so we hold generated client content to the same standard.
BUYER QUESTIONS
Written for the person who has to sign off the budget and defend it later. Every answer stays visible on the page, and the structured data matches it word for word.
A generative AI application produces output for a person: a draft, a summary, a classification, an extracted record, an image description. An AI agent completes a multi-step process in your systems, taking actions and handling failures along the way. The engineering overlaps considerably—both need model routing, evaluation and guardrails—but the measure of success differs. Generative features are judged on output quality, cost and latency; agents are judged on completed business outcomes.
Usually not. Fine-tuning adjusts how a model writes and behaves; it is poor at teaching facts, cannot cite sources, must be redone whenever your information changes, and locks you to the base model you tuned. If you need the system to know things specific to your organisation, retrieval is almost always the correct answer. Fine-tuning genuinely earns its cost for consistent output format, a distinctive tone, or a narrow classification task where you have abundant labelled examples.
Anthropic Claude, OpenAI GPT and Google Gemini models, plus open-weight and self-hosted options where data residency or cost requires them. We put a provider abstraction between your application and any model API and route by task type, so simple work goes to smaller, faster and cheaper models and only genuine reasoning goes to the capable ones. We benchmark on your evaluation set rather than on public leaderboards, because those rarely reflect your specific workload.
Usually, and often by an order of magnitude. Cost problems are typically design problems rather than pricing problems: sending an entire document when three paragraphs would do, regenerating identical outputs instead of caching, using the largest model for formatting work, or retry loops without a ceiling. We review the request pattern, then apply caching, task-based routing, prompt compression, batching and token ceilings—and only if none of that is enough do we conclude the feature is uneconomic.
With an evaluation harness built before any prompt optimisation: a fixed set of real inputs with expected outputs, including adversarial and must-refuse cases, scored automatically and run before every release with a regression gate. We also re-run it after provider model updates, because the same prompt can behave differently after a change you were never told about. Every reported failure is added to the set so it cannot silently return.
Yes. Document parsing, image and scan understanding, screenshot interpretation and audio transcription are all standard inputs now, which matters particularly in India where a lot of business information arrives as a photograph on WhatsApp. The important caveat is that extraction from an image is a probabilistic reading rather than a scan, so anything consequential gets per-field confidence scoring, validation against your existing records and a review queue for low-confidence items.
Layered controls. Output filtering enforces the categories you nominate as prohibited, independent of prompting. Automated quality checks score brand tone, factual consistency against source inputs, banned claims and length before anything reaches a person. Borderline output routes to a review queue with the failing check highlighted. Those refusal and quality cases are part of the evaluation set, so they are verified before every release rather than assumed.
Increasingly, yes. The EU AI Act transparency duties covering the marking of AI-generated content apply from 2 August 2026, and if you serve EU audiences they can apply to you. Independently of regulation, we attach generation metadata—model, prompt version, timestamp, reviewer—to every produced asset. That is a compliance measure and a practical one: when an issue is found, you can identify every other item produced the same way.
The feature degrades rather than breaks. The provider abstraction supports automatic fallback to an alternative model, cached results serve repeated requests, and where no fallback is possible the interface fails clearly and tells the user rather than spinning indefinitely. Rate limits, timeouts and retries with ceilings are all defined behaviours tested under realistic concurrency, not assumptions verified with single-user testing.
A focused feature typically runs eight to fourteen weeks: one to two weeks on use case and feasibility, one week building the evaluation set, two weeks on architecture, routing and cost modelling, then build, hardening under realistic load and a controlled launch. Taking an existing prototype to production quality is often faster, because the product questions are already answered and the work concentrates on evaluation, cost, reliability and safety.
RELATED EXPERTISE
Our AI offer runs in two directions: visibility inside AI search, and the systems that do the work. These links make that hierarchy explicit for people and crawlers.
Bring us the process that costs you the most hours or the most lost enquiries. We will scope it honestly, tell you if AI is the wrong tool for it, and price the smallest version that can prove itself in production.