[MACHINE LEARNING DEVELOPMENT SERVICES]
A model that scores well offline and fails in week two is the normal outcome, not the unlucky one. We build the data path, the evaluation set and the retraining schedule around the model first, so what you deploy keeps its accuracy after the launch deck is filed.
Six engagements, each scoped so you can tell whether it worked. We will say which one your problem actually needs, including the cases where the answer is none of them — a well-specified rules engine beats a model on more business problems than anyone selling models will admit, and costs a fraction as much to run.
Models built for the data you actually hold rather than the data a paper had. We agree the held-out evaluation set before any training starts, so a result cannot be talked up afterwards, and we report calibration alongside accuracy because the business decision depends on it more than on the headline figure. Where a smaller model distilled from a larger one holds the number at a tenth of the inference cost, that is what ships.
Classification, extraction, summarisation and search over your own documents in the formats they genuinely arrive in: scanned, mis-OCR'd, half-structured. Accuracy is measured per field against a human-labelled set rather than per document, because one wrong field in an otherwise perfect extraction is still a rework for somebody downstream.
Forecasting and scoring where latency is a design constraint from the first sprint rather than a late optimization. Batch and streaming paths are costed separately up front, because the choice between them decides the architecture and is far more expensive to reverse than to make. Confidence is calibrated and shipped with the prediction.
The work that makes a model usable: ingestion, the review queue, the audit trail, the admin surface and the integrations into whatever you already run. This is most of the engagement and almost none of the pitch. A model nobody can operate is not in production however good its F1 score, so the operating surface is built in the same sprint as the inference.
Detection, segmentation and OCR tuned on images from your own cameras, lighting and angles rather than a public benchmark — which is the difference between a demo and a number you can put in a report. Edge and server deployment are both costed before the build, since that choice usually decides everything after it.
Architectures chosen for your constraints rather than for novelty, with training, fine-tuning and distillation run against a set we both signed off. Retraining, drift detection and rollback are built alongside the model, so the second year of running it does not quietly cost more than the first.
What machine learning is worth depends on the process it lands in, so the figures that matter are the ones measured against your own baseline rather than an industry average.
These are the kinds of improvement production machine learning is built to deliver. Which of them is realistic for your data and your constraints is a conversation worth having before the statement of work rather than after it.
70%
Increase in prediction accuracy
35%
Reduction in operational costs
50%
Faster deployment of machine learning models
65%
Improvement in data processing speed
35%
Reduction in manual workload through automation
We start from the decision you are trying to make, not a feature list. The regulatory surface, the integration nobody owns and the number that would count as success all get named in week one.
The unglamorous half, and the half that decides whether the rest holds up. Ingestion, labelling, leakage checks, and a hard look at whether every feature will exist at inference time.
The signals that actually carry information, separated from the ones that only correlate in the sample you happen to hold. Validated against a held-out set agreed before anyone had a result to defend.
Two or three candidates benchmarked against your task rather than a public leaderboard, with cost per inference tracked as closely as accuracy. On tabular problems the unglamorous method usually wins.
Training, tuning and calibration, with every change running the evaluation suite so a quiet ten-point regression cannot reach production unnoticed. Calibration ships with the accuracy figure.
Bias measured per segment, failure modes red-teamed, and the rollback tested before the release rather than during the incident. If the numbers do not clear the bar, we say so and the call is yours.
Eight capabilities we have taken to production more than once. Each is listed with what it is genuinely good at rather than what it demos well at, because the gap between those two is where most ML budgets go.
Chosen for the data you have rather than for novelty, with distillation where a smaller model holds the accuracy at a fraction of the inference cost.
Throughput and cost measured under load before launch rather than discovered by it, with batch and streaming paths costed separately up front.
The boring baseline is run first and kept if it wins. Where it does not, the model that replaces it has to beat it on cost per task as well as on accuracy.
Synthesis and resampling for the classes your dataset is thin on, which is usually the handful that actually matter. Validated against real held-out examples, never against more synthetic ones.
Searched against the evaluation set agreed before training, with the compute budget fixed in advance so tuning stops at a number rather than when someone loses interest.
Every decision keeps what it read and what it scored, so a regulator's question is answered by replaying the run instead of reconstructing it from memory.
Residency, access and retention settled at architecture rather than at review, and the serving path treated as production software: versioned, rollback-ready and monitored.
New labelled examples fed back on a schedule so accuracy climbs rather than decays, with drift detection and rollback built in the same sprint as the model.
We benchmark two or three of these against your task rather than defaulting to whichever is in the headlines, and we report cost per task alongside accuracy. On narrow problems the smaller, cheaper model wins often enough that we always test for it.
Six things you can check rather than take on trust. Ask any supplier for the evaluation set they agreed before training, the engagement where they recommended not building, and the monthly running cost of the last model they put into production. The answers are usually more revealing than the case studies are.
Engineers and scientists who have taken models to production in regulated settings and then maintained them through a framework change and a shift in product direction. Employed rather than subcontracted, so the names in the proposal are the names on the work, and the lead who scoped it is the lead who delivers it.
Built for your data, your constraints and your failure cases rather than assembled from a template. We benchmark two or three approaches against your own labelled set before committing to one, and we report the approach that lost as well as the one that won.
Two-week iterations, each ending with something you can run rather than a status update. A written decision log that says what we chose and what it ruled out, so the reasoning survives the people who made it, and estimates revised out loud the week reality changes them.
Nine regulated sectors, and enough history in each to know which constraints are real and which are habit. That is mostly what decides whether a model is worth building at all, and it is the judgement you are paying for rather than the training code.
Drift detection, cost tracking and a monitored quality budget from day one, plus a monthly note on what moved and what we recommend next — written for the person who has to repeat it to a board, not for another engineer. Month to month, never an annual lock-in.
Residency, retention and access settled at architecture rather than at security review. Your data is excluded from third-party model training by configuration and by contract, and the audit trail answers a regulator by replaying a run instead of reconstructing it.
Bring us the decision you are trying to make, the data you hold and the one constraint you cannot move. You get a scoped range and the reasoning behind it in writing within a week — and if the honest answer is that a rules engine does this for a tenth of the price, you get that instead.
The reviews below are on Clutch, where the client is verified independently and we cannot edit, reorder or remove what they wrote. We have linked the most recent engagement of this shape rather than the best-scoring one, so you can read the schedule and cost marks alongside the quality mark — including where we were marked down.
$10,000 to $49,999
Dec 2024 – Sep 2025
A forecasting model taken from a feasibility read into production, with the evaluation set agreed before training and drift watched monthly after launch.
5.0
Quality
5.0
Schedule
5.0
Cost
5.0
Refer
5.0
“They really took the time to understand our business, our customer base, and our goals.”
Published on Clutch, where the full review and its ratings can be read in context.
President, Supplement King
Canada
11-50 Employees
Verified
A feasibility read is two to three weeks. A first model in production is usually three to five months, and most of that is the data path rather than the training. An enterprise rollout with a security review behind it adds a month or two, and the paperwork rather than the engineering sets that floor.
Tell us what you are building, who it is for and when it needs to be live. You will get a written scope, a realistic range, and a named engineer on the call rather than a sales deck.
[MACHINE LEARNING DEVELOPMENT SERVICES]
A model that scores well offline and fails in week two is the normal outcome, not the unlucky one. We build the data path, the evaluation set and the retraining schedule around the model first, so what you deploy keeps its accuracy after the launch deck is filed.
Six engagements, each scoped so you can tell whether it worked. We will say which one your problem actually needs, including the cases where the answer is none of them — a well-specified rules engine beats a model on more business problems than anyone selling models will admit, and costs a fraction as much to run.
Models built for the data you actually hold rather than the data a paper had. We agree the held-out evaluation set before any training starts, so a result cannot be talked up afterwards, and we report calibration alongside accuracy because the business decision depends on it more than on the headline figure. Where a smaller model distilled from a larger one holds the number at a tenth of the inference cost, that is what ships.
Classification, extraction, summarisation and search over your own documents in the formats they genuinely arrive in: scanned, mis-OCR'd, half-structured. Accuracy is measured per field against a human-labelled set rather than per document, because one wrong field in an otherwise perfect extraction is still a rework for somebody downstream.
Forecasting and scoring where latency is a design constraint from the first sprint rather than a late optimization. Batch and streaming paths are costed separately up front, because the choice between them decides the architecture and is far more expensive to reverse than to make. Confidence is calibrated and shipped with the prediction.
The work that makes a model usable: ingestion, the review queue, the audit trail, the admin surface and the integrations into whatever you already run. This is most of the engagement and almost none of the pitch. A model nobody can operate is not in production however good its F1 score, so the operating surface is built in the same sprint as the inference.
Detection, segmentation and OCR tuned on images from your own cameras, lighting and angles rather than a public benchmark — which is the difference between a demo and a number you can put in a report. Edge and server deployment are both costed before the build, since that choice usually decides everything after it.
Architectures chosen for your constraints rather than for novelty, with training, fine-tuning and distillation run against a set we both signed off. Retraining, drift detection and rollback are built alongside the model, so the second year of running it does not quietly cost more than the first.
What machine learning is worth depends on the process it lands in, so the figures that matter are the ones measured against your own baseline rather than an industry average.
These are the kinds of improvement production machine learning is built to deliver. Which of them is realistic for your data and your constraints is a conversation worth having before the statement of work rather than after it.
We start from the decision you are trying to make, not a feature list. The regulatory surface, the integration nobody owns and the number that would count as success all get named in week one.
The unglamorous half, and the half that decides whether the rest holds up. Ingestion, labelling, leakage checks, and a hard look at whether every feature will exist at inference time.
The signals that actually carry information, separated from the ones that only correlate in the sample you happen to hold. Validated against a held-out set agreed before anyone had a result to defend.
Two or three candidates benchmarked against your task rather than a public leaderboard, with cost per inference tracked as closely as accuracy. On tabular problems the unglamorous method usually wins.
Training, tuning and calibration, with every change running the evaluation suite so a quiet ten-point regression cannot reach production unnoticed. Calibration ships with the accuracy figure.
Bias measured per segment, failure modes red-teamed, and the rollback tested before the release rather than during the incident. If the numbers do not clear the bar, we say so and the call is yours.
Eight capabilities we have taken to production more than once. Each is listed with what it is genuinely good at rather than what it demos well at, because the gap between those two is where most ML budgets go.
Chosen for the data you have rather than for novelty, with distillation where a smaller model holds the accuracy at a fraction of the inference cost.
Throughput and cost measured under load before launch rather than discovered by it, with batch and streaming paths costed separately up front.
The boring baseline is run first and kept if it wins. Where it does not, the model that replaces it has to beat it on cost per task as well as on accuracy.
Synthesis and resampling for the classes your dataset is thin on, which is usually the handful that actually matter. Validated against real held-out examples, never against more synthetic ones.
Searched against the evaluation set agreed before training, with the compute budget fixed in advance so tuning stops at a number rather than when someone loses interest.
Every decision keeps what it read and what it scored, so a regulator's question is answered by replaying the run instead of reconstructing it from memory.
Residency, access and retention settled at architecture rather than at review, and the serving path treated as production software: versioned, rollback-ready and monitored.
New labelled examples fed back on a schedule so accuracy climbs rather than decays, with drift detection and rollback built in the same sprint as the model.
We benchmark two or three of these against your task rather than defaulting to whichever is in the headlines, and we report cost per task alongside accuracy. On narrow problems the smaller, cheaper model wins often enough that we always test for it.
Six things you can check rather than take on trust. Ask any supplier for the evaluation set they agreed before training, the engagement where they recommended not building, and the monthly running cost of the last model they put into production. The answers are usually more revealing than the case studies are.
Engineers and scientists who have taken models to production in regulated settings and then maintained them through a framework change and a shift in product direction. Employed rather than subcontracted, so the names in the proposal are the names on the work, and the lead who scoped it is the lead who delivers it.
Built for your data, your constraints and your failure cases rather than assembled from a template. We benchmark two or three approaches against your own labelled set before committing to one, and we report the approach that lost as well as the one that won.
Two-week iterations, each ending with something you can run rather than a status update. A written decision log that says what we chose and what it ruled out, so the reasoning survives the people who made it, and estimates revised out loud the week reality changes them.
Nine regulated sectors, and enough history in each to know which constraints are real and which are habit. That is mostly what decides whether a model is worth building at all, and it is the judgement you are paying for rather than the training code.
Drift detection, cost tracking and a monitored quality budget from day one, plus a monthly note on what moved and what we recommend next — written for the person who has to repeat it to a board, not for another engineer. Month to month, never an annual lock-in.
Residency, retention and access settled at architecture rather than at security review. Your data is excluded from third-party model training by configuration and by contract, and the audit trail answers a regulator by replaying a run instead of reconstructing it.
Bring us the decision you are trying to make, the data you hold and the one constraint you cannot move. You get a scoped range and the reasoning behind it in writing within a week — and if the honest answer is that a rules engine does this for a tenth of the price, you get that instead.
Book Your ConsultationThe reviews below are on Clutch, where the client is verified independently and we cannot edit, reorder or remove what they wrote. We have linked the most recent engagement of this shape rather than the best-scoring one, so you can read the schedule and cost marks alongside the quality mark — including where we were marked down.
A feasibility read is two to three weeks. A first model in production is usually three to five months, and most of that is the data path rather than the training. An enterprise rollout with a security review behind it adds a month or two, and the paperwork rather than the engineering sets that floor.
None, before we have seen your data, and anyone who quotes a number at this stage is guessing. What we will do is agree a labelled evaluation set in the first fortnight and report against it per segment, so accuracy is something you can check rather than something we assert.
No, and if you have one we work alongside it rather than around it. The pipelines, evaluation sets and documentation run in your account and are written to be handed over — a lot of clients take the second year in-house, which is a good outcome rather than a lost account.
Monitored from the first day in production, with a quality budget, an owner and a response time attached. Retraining runs on a schedule rather than in a panic, and the rollback is tested before each release instead of during the incident that needs it.
Then we say so, and in roughly one engagement in five we do. A rules engine or a better-indexed database solves more business problems than a model does, at a fraction of the cost to build and to run. That recommendation is part of the discovery engagement, not an upsell we skip.
