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Artificial Intelligence

Deep Learning

We build deep-learning systems for the perception and sequence problems classical ML cannot reach — and we will tell you plainly when your problem is not one of them.

Overview

Deep learning is the right tool for a specific shape of problem: unstructured data — images, audio, video, text, complex time-series — at enough scale that the patterns are too intricate for a person to hand-engineer the features. A neural network learns those features itself, layer by layer, which is exactly why it beats classical methods on a photograph or a waveform and exactly why it is overkill for a spreadsheet. This is the service where we build those systems: the model, the training pipeline that produces it, and the serving path that runs it in production.

We will start most engagements by trying to talk you out of it, or at least by pressure-testing whether you need it. Deep learning is data-hungry and compute-hungry, slower to train, harder to debug and more expensive to run than the alternatives, and for a great many business problems — particularly anything living in tables and columns — a gradient-boosted tree will match or beat a neural network on a fraction of the data, cost and time. If your problem is structured and tabular, you almost certainly want our classical machine-learning work, not this. Deep learning earns its keep when the input is genuinely unstructured and there is enough of it, or enough of a pre-trained foundation to build on, to make the network worth the weight.

That last point is what makes modern deep learning viable for organisations that are not sitting on millions of labelled examples. You rarely train from scratch. A model already trained on a vast corpus — of images, of language, of audio — has learned general-purpose representations, and transfer learning lets you adapt that foundation to your specific task with a comparatively tiny dataset. Most of our work is fine-tuning and adapting pre-trained networks rather than raising one from random weights, because that is what turns "we would need a research lab and a warehouse of GPUs" into "we can ship this in weeks on data you already have".

Who it’s for — Teams with a genuine perception or sequence problem — vision, audio, video, language or complex time-series — at enough scale that hand-engineered features have run out of road, and who want a straight answer on whether deep learning is worth its cost before they commit to it.

What you get

  • A trained deep-learning model that solves the actual perception or sequence task — validated on held-out data that reflects real conditions, not a flattering benchmark
  • A reproducible training pipeline — data preparation, augmentation, the training loop, checkpointing and evaluation — so the model can be retrained and improved rather than being a one-off artefact nobody can rebuild
  • Transfer learning and fine-tuning of an appropriate pre-trained model where one exists, so you are not paying to relearn what the field already knows
  • A serving path that fits the workload — a GPU or CPU inference service, batch scoring, or an optimised model compiled to run on edge or mobile hardware
  • Honest evaluation against a simpler baseline, so you can see what the deep-learning approach actually buys over classical ML — and whether it justifies its cost
  • Monitoring for the failure modes that matter — latency, throughput, cost per inference, and drift as real-world inputs wander from the training distribution
  • A handover with the weights, the code, the training data lineage and the documentation, so the model is yours to run, retrain and extend without us

What Deep Learning does for you

  • The right tool, honestly chosen

    The single most valuable thing we bring to a deep-learning conversation is the willingness to say when it is the wrong answer. Deep learning is spectacular on unstructured data at scale and genuine over-engineering on most tabular business problems, where classical machine learning wins on less data, less compute and less time. Choosing correctly between the two saves you from either a weak model built with the wrong tool or a ruinously expensive one built for a problem that never needed it.

  • Viable without a research budget

    Transfer learning is what makes deep learning accessible to organisations that are not technology giants. Because a pre-trained model has already learned general representations from an enormous corpus, adapting it to your task needs orders of magnitude less data and compute than training from scratch. That is the difference between a problem that would demand a research team and a data-labelling programme, and one we can solve in weeks on the data you already hold.

  • Built to run, not just to demonstrate

    A model that scores well in a notebook and a model you can afford to run at production volume are not the same thing. We size the serving path to the real workload, measure cost per inference and latency under load, and optimise — quantisation, distillation, the right hardware — when the numbers demand it. A network that is too slow or too costly to serve is not a solved problem, and we treat inference economics as part of the deliverable rather than a surprise you meet after launch.

Why teams choose us for Deep Learning

  • You want the deep-learning-versus-classical-ML decision made by engineers who will genuinely recommend the cheaper, simpler classical approach when it fits — rather than a team whose only tool is a neural network and who will therefore find every problem looks like one.
  • You need transfer learning and fine-tuning done well — the right pre-trained model chosen, the right layers adapted, the learning rates and augmentation that make a small dataset go far — rather than a from-scratch training run that burns weeks of GPU time relearning the obvious.
  • You care what the model costs to run, not just how it scores, and you want the serving path — GPU, CPU, batch or edge — chosen and optimised for your actual latency, throughput and budget instead of left as an afterthought.
  • You want an honest evaluation you can trust — measured on data that reflects real conditions, benchmarked against a simpler baseline, and reported with its failure modes named — rather than a single flattering accuracy figure that falls apart in production.

What Deep Learning includes

The concrete pieces of work this covers — scoped to what your problem actually needs.

  • Computer vision with convolutional and vision-transformer networks

    Image and video work — classification, object detection, segmentation, and reading content from frames — built on convolutional networks or vision transformers depending on the data and the task. Almost always via transfer learning from a backbone pre-trained on a large image corpus, then fine-tuned on your images, which is what lets a visual-inspection or detection model reach useful accuracy on the hundreds or low thousands of examples you can realistically label rather than the millions training from scratch would want.

  • Sequence and time-series modelling

    Problems where order and long-range dependency matter — sequences with intricate structure, time-series that classical models have plateaued on. We use recurrent architectures such as LSTMs where they fit and transformer-based sequence models where the dependencies are long or the data is plentiful, and we are candid that for many time-series problems a well-built classical model or gradient-boosted approach is the better answer, so this capability is applied where the sequence structure genuinely earns it.

  • Natural-language understanding with transformers

    Text tasks beyond keyword matching — classification, extraction, understanding and generation — built on pre-trained transformer language models fine-tuned to your domain. Because meaning in language depends on context that shallow methods cannot see, a transformer that has already learned the structure of the language and is then adapted to your task is the approach that works, and transfer learning keeps it viable on domain-specific datasets that are far too small to train a language model from nothing.

  • Audio and speech models

    Working directly with audio as a signal — transcription, sound-event detection, audio classification, keyword spotting — where the input is a waveform or spectrogram rather than a neat feature vector. We build on pre-trained audio and speech models where they exist and adapt them to your acoustic conditions and vocabulary, because the general representations they have already learned are exactly what a modest domain dataset cannot produce on its own.

  • Transfer learning, fine-tuning and training pipelines

    The core craft: selecting an appropriate pre-trained model, deciding which layers to freeze and which to adapt, and setting up the training loop, data augmentation, checkpointing and evaluation so a run is reproducible rather than a one-off nobody can rebuild. We work in PyTorch and TensorFlow, choosing per project, and we build the pipeline so the model can be retrained as new data arrives instead of ageing quietly until it no longer reflects reality.

  • GPU training and inference optimisation

    Training deep networks efficiently on GPU hardware, and — just as important — making the resulting model economical to serve. When a model is too slow or too costly at production volume we optimise it: quantisation to lower-precision arithmetic, distillation into a smaller network, pruning, and compilation to the target hardware, including edge and mobile devices where the model has to run within tight memory and power budgets rather than on a datacentre GPU.

Where it fits

  • Automated visual inspection

    A vision model that examines images or video frames and flags what matters — defects, categories, presence or absence of a feature — where the visual variation is too rich for hand-written rules. Built by fine-tuning a pre-trained vision backbone on your labelled images, it reaches useful accuracy on a dataset you can realistically produce, and is served either as a GPU inference service or, where the inspection happens on-site, compiled to run on edge hardware next to the camera.

  • Domain-specific document and text understanding

    Extracting structure and meaning from free-form text — classifying documents, pulling out fields, understanding intent — where the language is domain-specific and simple keyword rules break on the first unusual phrasing. A pre-trained transformer fine-tuned on your documents captures the context that shallow methods miss, and because it starts from a model that already understands the language, it works on a corpus far smaller than training a language model from scratch would demand.

  • Audio classification and transcription in real conditions

    Turning audio into something a system can act on — transcribing speech, detecting specific sounds or events, classifying recordings — under the messy acoustics of the real world rather than a clean studio. We adapt pre-trained audio and speech models to your vocabulary and recording conditions, because the general acoustic representations they carry are precisely what a modest, domain-specific dataset cannot learn on its own.

  • A sequence problem classical ML has plateaued on

    A pattern-recognition task over sequences or time-series where a classical model was the right first move, was built properly, and has genuinely run out of headroom despite real effort. Here a deep sequence model can capture the long-range or intricate structure the simpler approach cannot — but we take this on only after confirming the classical baseline was done well, because "deep learning next" is a sound decision after a real plateau and an expensive mistake before one.

How we approach Deep Learning

We anchor every deep-learning engagement to a baseline, because a neural network that beats nothing is not a result. Before we reach for a heavy architecture we establish what a simpler approach achieves — a classical model, a pre-trained model used off the shelf, sometimes a well-chosen heuristic — and treat that as the number to beat. Deep learning only stays in the engagement if it clears that bar by a margin that justifies the training cost, the inference cost and the added operational complexity. That discipline is how we avoid handing you an expensive model that a gradient-boosted tree would have matched for a tenth of the outlay.

When the problem does warrant it, we reach for transfer learning first and training from scratch last. We start from a pre-trained network suited to the data — a vision backbone, a language or audio transformer — and adapt it to your task, because that is what makes the work viable on the data you actually have rather than the data a research paper had. Training from random weights is reserved for the rare case where no relevant pre-trained model exists and you have the scale to justify it, and we will tell you when you are in that case rather than quietly billing you for it.

How the engagement runs

We open by interrogating the fit, not the architecture. The first questions are what the data actually is — is it genuinely unstructured, and is there enough of it — and what a simpler approach already achieves, because those answers decide whether deep learning belongs in the engagement at all. We establish a baseline early: a classical model, a pre-trained model used off the shelf, or a heuristic, so there is a concrete number the neural network has to beat. If the baseline is already good enough, the honest outcome is that you do not need this service, and we would rather reach that conclusion in week one than after a training budget is spent.

When deep learning is warranted, we move to transfer learning first. We select a pre-trained model suited to the data, build the training pipeline — data preparation, augmentation, the training loop, checkpointing and evaluation — and fine-tune to the task, iterating against a held-out set that reflects real conditions rather than a flattering slice. As the model reaches useful accuracy we turn to serving: sizing the inference path to the real workload, measuring cost per inference and latency, and optimising where the numbers demand it. Throughout, every improvement is measured against the baseline and the previous best, so progress is a real gain rather than a hopeful hunch that this run felt better.

How we architect it

A deep-learning system has two distinct halves, and we architect them separately. The training side is a reproducible pipeline: versioned data with clear lineage, augmentation, the training loop, checkpointing so a long run can survive interruption, and evaluation baked in so every candidate model is scored consistently. It runs on GPU hardware — cloud instances sized to the job, spun up for training and down again to avoid paying for idle accelerators — and it is built so the model can be retrained as data arrives rather than being a one-off artefact nobody can reproduce.

The serving side is a different problem with different constraints, and we size it to the workload rather than defaulting to an always-on GPU. Where inference is occasional or tolerant of delay, batch scoring or a CPU service is far cheaper. Where it must be real-time and high-throughput, a GPU inference service earns its cost, and we optimise the model — quantisation, distillation, compilation — to keep latency and cost per inference within budget. Where the inference has to happen on-device, at the edge or on mobile, we compile and shrink the model to run within tight memory and power limits, accepting the accuracy trade-off that comes with it deliberately rather than discovering it in the field.

Security

Deep-learning systems concentrate risk in two assets: the data and the model. Training data is frequently sensitive — images of people, recordings of voices, private documents — and it does not merely pass through the system, it is baked into the weights, which means data governance has to cover the training pipeline and the resulting model, not just the live inputs. We are careful about where training data lives, who can reach it, and how it is retained, and where data residency or confidentiality rules it out, we train on your own infrastructure rather than shipping your data to a third party. A model can also memorise and leak fragments of its training data, so we treat that as a real consideration for any model trained on personal or confidential material.

The model itself is an asset to protect and a surface to defend. The weights represent both your investment and, potentially, the sensitive data they learned from, so we handle them with the same care as any other secret — access-controlled, not casually copied to a laptop or a public bucket. On the inference side, every input is untrusted: we validate and bound what reaches the model, and where the stakes justify it we account for adversarial inputs designed to fool the network and for the model degrading silently as real-world data drifts from the training distribution. We build the monitoring to catch that drift, because a model that has quietly stopped being accurate is its own kind of security failure — one that produces confident, wrong answers with no error to alert you.

Signs it’s time

  • You have an image, video or visual-inspection problem — classifying, detecting, segmenting or reading content in pictures or frames — where the variation is too rich for hand-written rules to keep up
  • You are working with audio or speech — transcription, classification, keyword spotting, sound-event detection — where the signal is a waveform rather than a tidy set of features
  • You have a language or text problem beyond simple keyword matching — understanding, extraction, classification or generation over free-form text — where meaning depends on context a bag-of-words model cannot see
  • You have a complex sequence or time-series pattern — one with long-range dependencies or intricate structure — that classical models have plateaued on despite genuine effort to make them work

Our working method

The organising principle is that deep learning must earn its place against a simpler alternative, so a baseline is the first thing we build and the number every later decision answers to. This is deliberate resistance to the field’s central temptation — reaching for the most powerful, most fashionable architecture before establishing that the problem needs it. For structured, tabular data we will actively steer you towards classical machine learning, because a gradient-boosted model there wins on data, cost and time, and recommending the heavier tool anyway would be selling you complexity you do not need. Deep learning stays in the engagement only where the data is genuinely unstructured and at sufficient scale, and only where it beats the baseline by a margin worth its weight.

When it does belong, the method favours adaptation over invention: transfer learning from a pre-trained foundation first, training from scratch only in the rare case where no relevant pre-trained model exists and the scale justifies it. We measure relentlessly on data that reflects real conditions rather than a convenient benchmark, we name a model’s failure modes rather than hide behind a single accuracy figure, and we treat inference cost and latency as first-class results because a model too expensive to serve has not actually solved anything. And because the engineers who build the model are the ones who reason about running it, the operational realities — retraining, drift, serving economics — shape the work from the start rather than arriving as an unwelcome surprise.

Technologies we build it with

Chosen per problem, not per fashion — this is the stack we most often reach for on this work.

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

What changes

  • A model that clears the baseline

    A deep-learning system measured against a simpler alternative and kept only because it beats it by a margin that justifies the cost — not adopted because it is fashionable.

  • Viable on your data

    Transfer learning from a pre-trained foundation, so the network is trainable on the dataset you already have rather than the millions of examples training from scratch would demand.

  • Running in production

    A serving path sized to the workload — GPU service, batch job or an edge-optimised model — with cost per inference and latency measured and kept within budget, not discovered after launch.

Industries we serve

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

How pricing works

  • A feasibility and baseline engagement, priced by the assessment, where we establish whether your problem genuinely needs deep learning, what a simpler approach achieves, and what a fine-tuned model could realistically add — so you commit to the full build knowing what it buys before the GPU meter starts running.
  • A fixed-scope model build for a well-defined perception or sequence task, quoted once the data, the baseline and the target are understood, covering the training pipeline, the fine-tuned model and a serving path sized to the workload.
  • A monthly senior engagement for ongoing deep-learning work — retraining as data grows, tackling drift, extending to new tasks or optimising inference cost — where the model is a living system that needs continuity rather than a one-off deliverable.
  • A focused optimisation or rescue engagement for an existing model that works but costs too much or runs too slowly to serve, or one that has drifted out of accuracy — priced by the specific problem rather than a full rebuild.

Typical timeline

  1. 01

    Feasibility, data review and baseline

    One to two weeks assessing whether the data is genuinely suited to deep learning, establishing what a simpler baseline achieves, and setting the concrete number a neural network has to beat — the point at which we will tell you honestly if you do not need this at all.

  2. 02

    Transfer learning and first trained model

    Two to four weeks selecting an appropriate pre-trained model, building the training pipeline and fine-tuning to the task, producing a first model measured against the baseline on data that reflects real conditions.

  3. 03

    Iteration to useful accuracy

    Successive cycles of augmentation, architecture and training refinement, each measured against the previous best, until the model clears the bar that justifies its cost — or until we conclude honestly that it cannot, before more budget is spent chasing it.

  4. 04

    Serving, optimisation and handover

    Deploying the model on a serving path sized to the workload — GPU, CPU, batch or edge — optimising where cost or latency demands it, wiring in drift and performance monitoring, and handing over the weights, code, data lineage and documentation so the model is yours to run and retrain.

Why teams choose us for Deep Learning

  • We will talk you out of it when it is wrong

    Most teams selling deep learning have only deep learning to sell, so every problem conveniently needs a neural network. We do classical machine learning too, which means our recommendation is genuine: for tabular, structured problems we will steer you to the cheaper, simpler approach that actually wins there, and reserve deep learning for the unstructured problems at scale where it truly earns its cost.

  • Transfer learning done properly

    The craft that makes deep learning viable without a research budget is knowing which pre-trained model to start from, which layers to adapt, and how to make a small dataset go far. We fine-tune where the field has already done the expensive learning, and reserve training from scratch for the rare case that genuinely warrants it — rather than burning weeks of GPU time relearning what a backbone already knew.

  • We treat inference cost as part of the result

    A model that scores beautifully in a notebook but costs too much or runs too slowly to serve has not solved your problem. Because we operate what we build, serving economics — the right hardware, quantisation, distillation, edge compilation — are part of the engagement from the start, so you get a model you can actually afford to run at volume rather than one that only works on a spare GPU.

  • Senior engineers who measure honestly

    Deep learning is unusually easy to fool yourself with — a flattering benchmark, a leaky validation split, an impressive number that collapses in production. The people building your model have felt those traps and evaluate against them: real-world data, a simpler baseline to beat, and failure modes named out loud rather than a single accuracy figure held up as proof.

How to engage us

Three ways to work with us on this — chosen to fit the problem, not our margin.

Related services

Part of AI Development. Other work we do alongside this.

Common questions

How do I know whether I need deep learning or ordinary machine learning?

The clearest signal is the shape of your data. If it lives in tables and columns — structured, tabular data — classical machine learning such as gradient boosting will almost always match or beat a neural network on far less data, cost and time, and deep learning there is genuine over-engineering. Deep learning earns its place when the input is unstructured — images, audio, video, free-form text, complex sequences — at enough scale that hand-engineered features cannot keep up. If you are not sure which camp your problem is in, that is exactly what our feasibility engagement establishes, and we would rather tell you to use the simpler tool than sell you the heavier one.

We do not have millions of labelled examples. Can we still use deep learning?

Very probably, thanks to transfer learning. You rarely train a network from scratch; instead you start from a model already trained on an enormous corpus that has learned general-purpose representations, and adapt it to your task with a comparatively tiny dataset — often hundreds or low thousands of labelled examples rather than millions. That is what makes modern deep learning viable for organisations that are not technology giants. Training from random weights is reserved for the rare case where no relevant pre-trained model exists and you have the scale to justify it, and we will tell you plainly if you are in that case rather than assuming it.

What does it actually cost to run a deep-learning model in production?

It depends entirely on the workload, and it is a question we answer with numbers rather than hand-waving. A model serving occasional or delay-tolerant requests can run as a cheap batch job or on CPU; one that must be real-time and high-throughput needs GPU inference, which is the expensive case. When cost or latency is a problem we optimise — quantising the model to lower precision, distilling it into a smaller network, or compiling it for the target hardware — often cutting the serving bill substantially with little accuracy loss. We treat inference economics as part of the deliverable, so you know what running the model costs before you commit to it, not after.

Can the model run on a device or at the edge rather than in the cloud?

Often, yes, and it is a good fit when latency, connectivity or data-privacy rules mean the inference should happen where the data is rather than in a datacentre. Running on edge or mobile hardware means fitting the model within tight memory and power budgets, which we achieve by compiling and shrinking it — quantisation, pruning, sometimes distillation into a smaller network. There is usually an accuracy trade-off in doing so, and we make that trade deliberately and measure it, rather than discovering in the field that the shrunk model is no longer good enough.

PyTorch or TensorFlow — does it matter which you use?

For most projects the choice does not change the outcome, and we work fluently in both, choosing per engagement rather than out of loyalty. The decision is usually driven by practical constraints — the pre-trained models available for your task, your deployment target, and any framework your own team will need to maintain the model in afterwards. What matters far more than the framework is the surrounding engineering: a reproducible training pipeline, honest evaluation, and a serving path sized to your workload. A model is only as good as the pipeline that produced it and the monitoring that keeps it accurate, and those disciplines look much the same whichever framework underpins them.

Let’s talk about Deep Learning.

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