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AI & Machine Learning

LLM

Large language models are a remarkable capability and a poor source of truth. We build the retrieval, evaluation and guardrails that turn the model into something you can depend on.

Overview

A large language model is a neural network — specifically a transformer — trained on an enormous quantity of text to do one deceptively simple thing: predict the next token given everything before it. A token is a chunk of text, usually a word fragment, and the model works through language a token at a time, each prediction feeding the next. From that single objective, applied at vast scale, something surprising falls out: the model absorbs grammar, facts, style, reasoning patterns and the shape of countless tasks, so that the same network can summarise a report, draft an email, extract fields from a contract, or write code, without ever being explicitly programmed to do any of them. That generality is what makes LLMs genuinely new, and it is also what makes them easy to misjudge.

It helps to separate two things that people routinely conflate: the base model and the application around it. The base model is the trained network you send text to and get text back from — a frontier model hosted by a provider, or an open-weights model you run yourself. On its own it is a next-token predictor with a fixed knowledge cut-off, a bounded context window (the amount of text it can consider at once), and no memory of your business, your documents or the conversation you had five minutes ago. The application is everything you build around it: the retrieval that feeds it your data, the prompts that frame the task, the tools it can call, the validation that checks its output, and the evaluation that tells you whether any of it works. Almost all of the value, and nearly all of the difficulty, lives in the application, not the model.

The mature way to build with an LLM, and the one we hold to, is to treat the model as one capable but fallible, replaceable component in a larger system — never as an oracle. There are a handful of ways to put a model to work: prompting it well, grounding it in your data through retrieval-augmented generation, letting it call tools and act as an agent, and, more rarely than vendors imply, fine-tuning it. Which of these you reach for depends on the problem, and choosing wrongly is the commonest reason AI projects stall. Our job is to pick the right technique, wrap the model in retrieval, evaluation and guardrails, and keep the whole thing honest under real usage — because a convincing demo and a dependable product are separated almost entirely by that engineering.

Best for — Language-shaped problems — assistants, retrieval and search over your own content, extraction, classification, summarisation and code — where the output can be grounded and verified, and where a senior team supplies the retrieval, evaluation and guardrails that make a probabilistic model dependable.

Why teams choose LLM

  • One general capability, many tasks

    A single model can summarise, classify, extract, draft and answer questions without a bespoke system built for each. That breadth means you can address a whole class of language problems with one well-engineered foundation, rather than commissioning a separate tool for every job.

  • Handles the messiness of real language

    LLMs generalise across phrasing, tone, typos and edge cases that defeat rule-based systems. Where a traditional pipeline needs an ever-growing list of special cases, a grounded model copes with input it has never seen before, which is where most of the practical value comes from.

  • Fast from idea to working feature

    Because the capability is trained rather than programmed, you can have a real, testable feature running against your own data in days. That speed lets you validate whether an LLM actually solves your problem before committing to a large build — and lets us tell you early if it does not.

Why businesses choose LLM

  • Your problem is language-shaped and the long tail of natural input matters — the exact case where a general model beats a pile of rules.
  • You need to ship a working feature quickly and want to prove the approach against real data before committing to scale.
  • You want the model treated as a replaceable component behind retrieval, evaluation and guardrails — not as a magic box you have to trust.
  • You have, or want us to provide, the engineering judgement to ground the model, measure its output, control its cost, and keep a human in the loop where the stakes require it.

What we build with LLM

The capabilities this technology is genuinely strong at — and what we most often build with it.

  • Assistants and conversational interfaces

    Chat-style features that answer questions, guide a user through a task, or sit alongside your product as help. We build these with clear system prompts, managed conversation context, and guardrails on what the model may say and do — because a helpful assistant and an unbounded liability differ only in the engineering around them.

  • Retrieval-augmented search and Q&A

    The dominant pattern for answering over your own content. We embed your documents into a vector database, retrieve the passages relevant to a question, and have the model answer grounded in them with citations back to source. This is how you get answers from your data rather than the model’s training, and how those answers stay checkable.

  • Structured extraction

    Turning unstructured input — contracts, emails, PDFs, forms — into clean, typed data by constraining the model to a JSON schema. Done well this replaces manual data entry with a pipeline whose output is guaranteed to parse and match the shape your systems already expect, with validation before anything downstream trusts it.

  • Classification and routing

    Using a model to categorise text — sentiment, intent, topic, priority — where the categories are fuzzy and the input varies too much for rules. We pair this with evaluation against labelled examples, because a classifier you cannot measure is a classifier you cannot trust, and with a fallback for the cases it is unsure about.

  • Summarisation and rewriting

    Condensing long documents, threads or call transcripts into the parts that matter, or rewriting content for a different audience or tone. We are candid that summarisation can quietly drop or distort a crucial detail, so we scope it to where an imperfect summary is useful and a human still has the source.

  • Code generation and understanding

    Models are strong at writing, explaining, translating and reviewing code. We build features that draft code, answer questions about a codebase, or automate routine engineering work — always with the output treated as a proposal to be reviewed and tested, never as code to be trusted unread.

Use cases

  • Answering over an internal knowledge base

    A retrieval-augmented assistant that answers staff or customer questions from your policies, documentation and support history, citing the source passage so every answer can be verified rather than believed. The commonest high-value LLM application, and the one that most rewards good retrieval.

  • Document processing at scale

    Reading a stream of unstructured documents — invoices, applications, contracts — and extracting validated, structured data for your existing systems. This turns a manual, error-prone data-entry task into a pipeline, with the schema and validation ensuring the output is clean enough to depend on.

  • Triage and classification of inbound text

    Sorting support tickets, emails or feedback by intent, urgency and topic so they reach the right place automatically. The model handles the phrasing variety that rules cannot, while low-confidence cases are routed to a human rather than guessed at.

  • Drafting and assistance in a workflow

    Embedding an LLM inside an existing process to draft a reply, propose a summary, or pre-fill a form, leaving a person to review and approve. The model does the tedious first pass; the human keeps judgement and accountability — the pattern that most often pays off without the risk of full automation.

When LLM is the right choice

  • Your problem is genuinely language-shaped — summarising, drafting, classifying, extracting structure from messy text, answering questions over your own documents, translating, or writing and explaining code. These are the tasks LLMs are strongest at, and where the return on the effort is clearest.
  • You can tolerate variation and verify the output. An LLM is right when a slightly different but still-good answer each time is acceptable, or when its output is checked — by a person, by deterministic code, or against a source of truth — before anything important depends on it.
  • You want to handle the long tail of natural language that rules and regexes never quite cover. Where a traditional system would need a thousand brittle special cases, a well-grounded model generalises across them, which is exactly where it earns its place.
  • Wrong for: anything that must be deterministic, exact and repeatable — pricing calculations, tax logic, financial postings, unit conversions, legal thresholds. These belong in ordinary code you can test and reason about. A model that is right 99% of the time is unacceptable where the answer must be right every time, and it will state the wrong 1% with total confidence.
  • Wrong for: safety-critical or high-stakes decisions with no human oversight, and for problems a simpler solution already solves. If a lookup table, a search index, a rules engine or a small classifier does the job, reaching for an LLM adds cost, latency and a new class of failure for no benefit. We will tell you when that is the case.

LLM: pros and cons

Strengths

  • Genuinely general: one model handles a wide range of language tasks with no task-specific training, which is unlike anything that came before it.
  • Strong at understanding and generating natural language, including messy, informal and previously-unseen input that breaks rule-based systems.
  • Programmable through tool calling and structured outputs, so the model becomes a component in software rather than just a chat box producing prose.
  • A fast path from concept to a working, evaluable feature, which makes it cheap to test whether the approach fits your problem at all.

Trade-offs

  • Hallucination and reliability: a model will state false things fluently and with complete confidence. It is not a database and has no notion of truth — only of plausible next tokens — so every factual claim it makes must be grounded and checkable, never trusted on its own.
  • Cost and latency: large models are slow and metered per token. Costs that are trivial in a prototype become a serious line item at volume, and generation takes real time, which shapes both the user experience and the architecture.
  • Data privacy: hosted models send your data to a third party, and self-hosting shifts the burden to running GPUs. Either way, what data reaches the model, and where, is a governance decision that has to be made deliberately rather than by default.
  • Non-determinism, prompt injection and confident error: the same prompt can produce different answers, malicious instructions hidden in documents or user input can hijack the model, and it fails in ways ordinary software does not. None of these are bugs to patch away — they are properties to engineer around.

Architecture

Building with an LLM is a software-engineering problem, and the architecture decides whether you end up with a dependable product or a fragile demo. The centre of gravity is not the model but the system around it. We treat the prompt as a designed, versioned artefact with a clear contract rather than a string someone tweaked until it looked good. Around it sits the retrieval layer that fetches the right context so the model answers from your data; the output validation that checks structured responses against a schema before anything trusts them; and the guardrails that bound what the model is allowed to see, say and do.

The single most important architectural decision is to choose the right technique for the problem, because they are not interchangeable. Prompting — framing the task well and giving examples — is the first tool and solves more than people expect. Retrieval-augmented generation is the answer when the model needs to know things it was not trained on, which is almost always the case for your business, and it is how you avoid the myth of stuffing everything into one giant prompt. Tool and function calling let the model call your code with structured arguments and is the foundation of agents — systems where the model plans, acts and works towards a goal. Fine-tuning, which adjusts the model’s weights on your examples, is the last resort, not the first: it is worth it to teach a consistent format or style, or to make a smaller model do one narrow task cheaply, but it does not reliably teach facts, and RAG usually solves the problem people reach for fine-tuning to fix.

Underpinning all of it, we put the model behind a thin internal interface so the rest of the application neither knows nor cares which model is serving a request. That is what lets us swap a hosted frontier model for another, or move a sensitive workload to a self-hosted open model, without rewriting the product. And we hold one rule absolutely: the LLM is never the source of truth. It drafts, extracts, classifies and proposes; deterministic code and your systems of record hold the facts.

Performance

LLM performance is mostly about latency and how you hide it. Large models generate a token at a time and take real, perceptible time to produce a full answer; reasoning-oriented models take longer still because they work through the problem before responding. For anything a user waits on, we stream the output so text appears as it is generated and the interface stays responsive even while the answer is still forming. We keep prompts and retrieved context as tight as they need to be and no tighter, because every unnecessary token costs both latency and money, and we pick the smallest model that clears the quality bar for each task rather than defaulting to the largest for everything.

The context window — how much text the model can consider at once — is a hard budget, not a limitless bucket, and quality degrades when you fill it with marginally-relevant material. Good retrieval that fetches a few precise passages beats dumping an entire corpus into the prompt, on both cost and accuracy. For work that is not interactive — bulk classification, embedding a large document set, overnight processing — we separate that path from the interactive one so each is optimised for what it needs. Above all we measure real latency and token consumption per feature under representative load, because the speed and cost of an LLM feature are things you engineer deliberately, not numbers you discover on the bill.

Security

An LLM introduces failure modes ordinary software does not have, and prompt injection is the one most teams underestimate. Because the model reads text and follows instructions, malicious instructions hidden in a document, a web page or user input can attempt to hijack it — to ignore its rules, exfiltrate data, or misuse a tool. This is a genuine, current threat, and it is most dangerous in retrieval and agentic systems where the model consumes untrusted content. We defend by never granting the model unmediated power: tool calls and generated actions are validated and authorised by our code before they run, model output never reaches a shell, a raw database query or a filesystem without checks, and an agent’s permissions are bounded so a successful injection cannot cause real damage.

The second concern is data. With a hosted model, requests leave your infrastructure and go to a third party, so data governance comes before code: we map what data actually needs to reach the model, minimise and redact where we can, and use zero-retention or enterprise arrangements where compliance requires them — and where even those are insufficient, we say so and design around it, up to and including a self-hosted open model. We guard against the model leaking one user’s data to another, keep credentials and keys server-side, and treat every output as untrusted until validated. Security here is not a feature bolted on at the end; it is the assumption the whole system is built under.

Scalability

What you scale against with an LLM is unusual: not CPU and memory in the ordinary sense, but rate limits, tokens and cost. With a hosted model the expensive, hard part — running the network — is the provider’s problem, so your application tier stays a normal, stateless, horizontally scalable service; what you must handle is throttling, with queuing, backoff and demand shaping so bursts do not hit the ceiling all at once. With a self-hosted open model the calculus flips: you own the GPU capacity, and scaling means provisioning and batching inference efficiently, which is a real operational commitment we are candid about.

Either way, cost scales with usage in a way ordinary compute does not, so scaling well means spending well. We route each task to the cheapest model that meets its quality bar, cache where inputs repeat, keep prompts and context lean, and push non-urgent work to bulk paths. Because the model sits behind a portability layer, we can also spread load across providers and models, both to stay under any single vendor’s limits and to move workloads on cost or capability grounds. The aim is a system that scales on traffic without the bill scaling faster than the value it delivers.

LLM integrations & ecosystem

The technologies we most often pair with it — each links to how we work with it.

How we work

We start by pressure-testing whether the problem is actually one an LLM should solve, because sometimes the honest answer is no — a simpler tool fits better, or the task needs the determinism a model cannot give — and it is far cheaper to hear that in week one than after a build. When it is a fit, we get a thin end-to-end slice working against real data quickly: one prompt, real retrieval, structured output validated, running in your application rather than a notebook. That slice reveals more about feasibility, quality and cost than any amount of planning, and we build it on the evaluation and guardrail scaffolding from the outset rather than bolting them on once a demo has already won everyone over.

From there we iterate with measurement, not vibes. An evaluation harness — representative inputs with known-good outputs — lets us tell whether a prompt tweak, a retrieval change or a new model actually helped, rather than guessing from a handful of examples. Cost and latency are tracked per feature; guardrails, fallbacks and prompt-injection defences are tested against the failure cases, not just the happy path. The engineers who design the system operate it, which is the point: we are the ones who will be paged when a model is deprecated, a bill spikes, or an injection is attempted, so we build for that reality from the first commit.

The service behind it

Delivered throughAI Development

What we build with LLM

The disciplines this technology most often shows up in — from a first build to taking over and stabilising an existing one.

How we deliver

  1. 01

    Discover

    We map the system, the constraints and the business it serves — including the parts nobody documented.

    Architecture brief

  2. 02

    Architect

    Decisions get made, written down and defended before a line of production code exists.

    Decision records

  3. 03

    Build

    Short cycles against working software. You see progress in the product, not in a status deck.

    Shipping increments

  4. 04

    Operate

    Monitoring, incident response and iteration. The system is alive, so the engagement is too.

    Runbooks & SLOs

Industries we use LLM in

Domain knowledge changes what gets built. A few of the sectors we know before the first meeting.

Also in AI & Machine Learning

Why teams choose us for LLM

  • We build systems, not prompts

    Anyone can get an impressive answer out of a model in an afternoon. The hard part — retrieval, evaluation, guardrails, cost control, handling non-determinism and failure — is where most LLM projects quietly break in production. That hard part is exactly what we do, because we are the ones who then have to keep the thing running.

  • We operate what we build

    We run the AI systems we ship, so we design for the on-call reality: rate limits handled, fallbacks in place, spend watched, model deprecations survivable, injections defended. You get a feature that stays useful and affordable under real traffic, not one that works in the pitch and surprises you on the invoice.

  • Honest about what a model is

    We treat the LLM as a capable but fallible component, never a source of truth. We are blunt about hallucination, about what must stay in deterministic code, and about data that should not go to a third party at all — and we build accordingly rather than pretending the model is more reliable than it is.

  • The model is a replaceable part

    We put the model behind a thin abstraction from day one, so you are never trapped by one vendor or one model. When a model is deprecated, a competitor pulls ahead, or a workload must move on-premise for compliance, you change a layer rather than rewrite the product.

Typical timeline

  1. 01

    Discovery and feasibility

    One to two weeks establishing whether an LLM genuinely fits the problem, what data it needs, which technique suits — prompting, retrieval, tools or fine-tuning — and the quality and compliance bar, with an honest answer if a model is the wrong tool here.

  2. 02

    End-to-end slice

    Two to three weeks building one real feature against real data — prompt, retrieval, structured output, validation and a first evaluation set — running in your application, so feasibility, quality and cost are proven against reality rather than a plan.

  3. 03

    Iterative build and evaluation

    Feature-by-feature delivery in short cycles, each measured against the evaluation harness, with guardrails, fallbacks, cost controls and prompt-injection defences built in as we go rather than added at the end.

  4. 04

    Hardening and handover

    Load and rate-limit testing, a security and data-governance review, cost profiling under real usage, and documentation of prompts, evals and the portability layer so your team can own, extend and swap models safely.

How pricing works

  • Fixed-scope builds for well-defined LLM features — a retrieval assistant, a document-extraction pipeline, a classification service — quoted once the data, the quality bar and the integration points are understood.
  • Monthly senior engagement for ongoing AI product work, where the scope evolves as you learn what the model can and cannot do for your users and you want continuity rather than a one-off deliverable.
  • Focused audits and rescues for existing LLM systems — a prototype that will not survive production, spiralling token costs, weak or absent evaluation, or a system that hallucinates in ways you cannot control — priced by the assessment.

Hire LLM engineers

Need LLM capacity on your own team? We embed named senior engineers into your existing team — reporting to your leads, working in your rituals — so you add capacity without a hiring cycle.

Hire LLM engineers

Common questions

What actually is a large language model — is it intelligent?

It is a transformer neural network trained on a vast amount of text to predict the next token given everything before it. From that single objective at scale, it acquires a striking general ability to work with language — summarising, drafting, reasoning through problems, writing code. Whether that constitutes intelligence is a philosophical question we would rather sidestep, because it distracts from the engineering one: the model is a powerful pattern-completer with no built-in notion of truth, no memory beyond its context window, and a fixed knowledge cut-off. Treating it as a capable component to be grounded and checked, rather than a mind that knows things, is what leads to systems that actually work.

How do you stop an LLM from making things up?

You cannot make a language model incapable of being wrong, so we engineer around it rather than pretend otherwise. Retrieval-augmented generation grounds answers in your actual documents, and we cite sources so claims can be checked. Structured outputs constrain the model to a schema so it cannot wander. An evaluation harness catches drops in accuracy before they ship. And critically, we never let the model be the source of truth — it drafts, extracts and proposes, while deterministic code and your systems of record hold the facts. For high-stakes outputs we keep a human in the loop. The goal is a system whose failures are bounded and visible, not a model that never errs.

Should we prompt, use retrieval, or fine-tune?

These solve different problems and the order matters. Prompting — framing the task well and giving examples — is the first tool and handles more than people expect. Retrieval-augmented generation is the right answer when the model needs to know things specific to your business, which is nearly always, and it is how you give the model your data without retraining anything. Fine-tuning, which adjusts the model’s weights, is a last resort: it is worth it to lock in a consistent format or style, or to make a small model do one narrow task cheaply, but it does not reliably teach facts, and RAG usually solves the problem people reach for fine-tuning to fix. Choosing wrongly here is the commonest reason AI projects stall, so we decide it deliberately rather than by fashion.

Should we use a hosted frontier model or self-host an open one?

For most teams starting out, a hosted frontier model is the sensible call: it is stronger than what you can easily self-host, and you avoid the cost and complexity of running GPUs. A self-hosted open-weights model earns its keep when data cannot leave your control for compliance reasons, when volume is high enough that per-token pricing outweighs infrastructure cost, or when you need control the API will not give you. Because we put the model behind a portability layer, this is not a one-way door — we can start hosted to validate the product and move specific workloads to an open model later without rewriting the application.

Why is the engineering around the model the expensive part?

Because the model is the easy 20% and the reliable system is the other 80%. Getting a good answer once is a demo; getting good answers consistently, over your data, at acceptable cost and latency, without hallucinating into a decision that matters, is a product. That means retrieval that finds the right context, evaluation that measures quality rather than guessing at it, guardrails against prompt injection and misuse, validation of every structured output, fallbacks for when the model or the provider fails, and a portability layer so you are not trapped. None of that comes from the model — it comes from disciplined engineering around it, which is precisely where the value, and our work, lives.

Building on LLM?

A technical conversation with the engineers who would do the work. If we are not the right fit, we will say so on the call.

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