[NATURAL LANGUAGE PROCESSING SERVICES]
Language models are only half of it. We build the retrieval, the labelled evaluation set and the human review path around them, so what ships can be measured per field and defended to whoever signs it off — not a demo that reads well and scores badly.
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-indexed search over documents you already hold solves more business problems than a custom model does, and costs a fraction of one.
Tone, intent and escalation risk read from the channels your customers actually use: tickets, reviews, call transcripts and chat. Tuned on your own labelled examples rather than a public corpus, because sarcasm, domain shorthand and industry complaints do not look like product reviews. We report per-segment accuracy, since an overall figure hides the group whose complaints are being misread as neutral.
Classification, extraction and summarisation over the documents you hold in the formats they genuinely arrive in: scanned, mis-OCR'd, half-structured, occasionally upside down. 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.
Language signals that complement the rules you already run rather than replacing them: inconsistent narratives, templated claims, descriptions that do not match the structured data beside them. Confidence is calibrated and shipped with the score, because a flagged claim is a decision somebody has to justify to the customer it belongs to.
Assistants scoped to what they can actually answer, with refusal behavior designed before the happy path rather than discovered by a customer. Grounded in your documentation, your policies and your ticket history, with containment and escalation measured weekly. When deflection stalls the fix is usually the knowledge base, not a bigger model, and we will say so.
The product around the model: ingestion, the review queue, the audit trail, the admin surface and the integrations into whatever you already run. This is most of the work 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.
Two to three weeks to an honest read on whether NLP is the right instrument, what your data can actually support and what it would cost. Roughly one engagement in five ends here with a recommendation not to build, which is the cheapest outcome available to you and the one nobody else quotes for.
The primitives underneath every engagement above. Most problems need three or four of them composed, and knowing which is most of the value of the first fortnight.
Most of what a business knows is written down somewhere nobody can query: contracts, tickets, call notes, email threads, PDFs a supplier sent in 2019. The value is not in the model that reads them, it is in deciding which questions are worth answering and proving the answers are right often enough to act on.
So we start with the decision you are trying to make, agree the labelled set that defines success, and only then choose the architecture. The repository, the evaluation sets and the documentation are yours from the first commit, which means the second year of this does not depend on us still being in the room.
Nine 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 NLP budgets are lost.
Transformer architectures chosen for the data you actually have rather than the data a paper had, with distillation where a smaller model holds the accuracy at a tenth of the inference cost.
Retrieval that understands the question rather than matching its words, grounded in your own corpus and scoped to cite its sources or return nothing at all.
Transcription on real audio — accents, crosstalk, hold music, bad lines — with your domain vocabulary weighted so names, SKUs and policy numbers come back right first time.
Fields pulled from contracts, claims and invoices and scored per field against a human-labelled set, because one wrong field in a perfect extraction is still a rework.
Forty-plus languages with your glossary and tone enforced, and a review path for the content where a mistranslation is a legal problem rather than an awkward sentence.
Summaries that keep what the reader has to act on and are evaluated for omission as well as for fluency, which is the failure mode that actually costs you.
Precision tuned against your own cost of a false positive, since a filter that blocks one genuine customer is usually worse for you than ten pieces of spam getting through.
Routing and first-line answers with a confidence threshold below which the query goes to a person, and containment measured weekly rather than claimed at launch.
Tone and escalation risk measured per segment, because an overall accuracy figure reliably hides the group whose complaints are being read as neutral.
Eight stages, each ending in something you can open rather than a status update. The decision to continue is taken at the end of every one of them, and stopping is a priced outcome rather than a failure — the expensive mistake is the pilot nobody will kill after it has missed its numbers twice.
We start from the decision you are trying to make, not a feature list. The regulatory surface, the integration nobody owns and the evaluation set that defines success are all named in week one.
The unglamorous half, and the half that decides whether the rest holds up. Labelling, leakage checks, and a hard look at whether every feature will exist at inference time.
Two or three candidates benchmarked against your task rather than a public leaderboard, with cost per task tracked as closely as accuracy. The cheaper model often wins.
The product around the model: ingestion, review queue, audit trail and the integrations into whatever you already run. Built in the same sprint as the inference, not after it.
Measured against the set we agreed in stage one, which is the point of agreeing it before anyone had a result to defend. Per-field and per-segment, never just an average.
Staged rollout behind a flag, with the rollback tested before the release rather than during the incident. Residency and retention settled long before this point.
Drift detection, cost tracking and a monitored quality budget, plus a monthly note on what moved — written for the person who has to repeat it, not for another engineer.
New labelled examples fed back on a schedule, so accuracy climbs rather than decays. You can take this over yourself: the pipelines and evaluation sets are yours throughout.
What an NLP feature has to do before it is worth shipping to the people who will rely on it every day.
Every response cites the document it came from, or declines. An assistant that cannot show its source is one nobody in a regulated business is allowed to act on.
Scores that are calibrated rather than merely high, so a threshold means something and the review queue gets the cases that genuinely need a person.
Forty-plus languages with your glossary and tone enforced, and a review path for the content where a mistranslation is a legal exposure rather than an awkward sentence.
Every run keeps what it read, what it scored and what it changed, so a regulator's question is answered by replaying it rather than reconstructing it months later.
Bring us the corpus, the question you want answered 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 search would do this for a tenth of the price, you get that instead.
What you get from an engagement beyond the model itself. Each of the six below is something you can check rather than take on trust, which is the only kind of claim worth making at this stage of a sales conversation.
Ingestion, labelling and lineage treated as the product they are. Most model failures we are called in to fix are data failures, and most were visible in week one.
Outputs shaped around the decision somebody has to take, with the confidence and the source attached, rather than a score that leaves the judgement exactly where it was.
Tone, intent and churn signal read from the channels your customers actually use, measured per segment so the group you are failing does not hide inside an average.
The dull, high-volume paths first, instrumented before any of them are replaced, so the saving is measured against a real baseline rather than an estimate in a proposal.
Messaging adapted to segment and history with the guardrails written first, because personalization that gets a name or a policy number wrong costs more than generic copy.
Forty-plus languages with your glossary enforced, and a human review path for the content where being approximately right is not good enough.
Six reasons that hold up after the first release, which is the only point at which any of them matter. Ask any supplier for the labelled set they agreed before training, the engagement where they recommended not building, and the monthly running cost of their last deployed model. The answers are more revealing than the case studies.
People who have taken language models to production in regulated settings and then maintained them through an SDK 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.
Tuned on your corpus, your vocabulary 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 one that lost as well as the one that won.
Cost per task tracked as closely as accuracy from the first sprint. On narrow problems the smaller, cheaper model frequently wins, and we always test for it — the model that takes the benchmark and loses the unit economics is not the one you want in production.
Residency, retention and access settled at architecture rather than at 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.
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. The retraining pipeline runs in your account and is documented well enough for your team to own it.
Throughput, latency and cost measured under load before launch rather than discovered by it. Batch and real-time paths costed separately, because the choice between them usually decides the architecture and is expensive to reverse afterwards.
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We will not quote a number before seeing your data, and anyone who does is guessing. What we will do is agree a labelled evaluation set in the first fortnight and report against it per field and per segment, so accuracy is something you can check rather than something we assert.
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.
[NATURAL LANGUAGE PROCESSING SERVICES]
Language models are only half of it. We build the retrieval, the labelled evaluation set and the human review path around them, so what ships can be measured per field and defended to whoever signs it off — not a demo that reads well and scores badly.
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-indexed search over documents you already hold solves more business problems than a custom model does, and costs a fraction of one.
Tone, intent and escalation risk read from the channels your customers actually use: tickets, reviews, call transcripts and chat. Tuned on your own labelled examples rather than a public corpus, because sarcasm, domain shorthand and industry complaints do not look like product reviews. We report per-segment accuracy, since an overall figure hides the group whose complaints are being misread as neutral.
Classification, extraction and summarisation over the documents you hold in the formats they genuinely arrive in: scanned, mis-OCR'd, half-structured, occasionally upside down. 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.
Language signals that complement the rules you already run rather than replacing them: inconsistent narratives, templated claims, descriptions that do not match the structured data beside them. Confidence is calibrated and shipped with the score, because a flagged claim is a decision somebody has to justify to the customer it belongs to.
Assistants scoped to what they can actually answer, with refusal behavior designed before the happy path rather than discovered by a customer. Grounded in your documentation, your policies and your ticket history, with containment and escalation measured weekly. When deflection stalls the fix is usually the knowledge base, not a bigger model, and we will say so.
The product around the model: ingestion, the review queue, the audit trail, the admin surface and the integrations into whatever you already run. This is most of the work 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.
Two to three weeks to an honest read on whether NLP is the right instrument, what your data can actually support and what it would cost. Roughly one engagement in five ends here with a recommendation not to build, which is the cheapest outcome available to you and the one nobody else quotes for.
The primitives underneath every engagement above. Most problems need three or four of them composed, and knowing which is most of the value of the first fortnight.
Most of what a business knows is written down somewhere nobody can query: contracts, tickets, call notes, email threads, PDFs a supplier sent in 2019. The value is not in the model that reads them, it is in deciding which questions are worth answering and proving the answers are right often enough to act on.
So we start with the decision you are trying to make, agree the labelled set that defines success, and only then choose the architecture. The repository, the evaluation sets and the documentation are yours from the first commit, which means the second year of this does not depend on us still being in the room.
See How it WorksNine 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 NLP budgets are lost.
Transformer architectures chosen for the data you actually have rather than the data a paper had, with distillation where a smaller model holds the accuracy at a tenth of the inference cost.
Retrieval that understands the question rather than matching its words, grounded in your own corpus and scoped to cite its sources or return nothing at all.
Transcription on real audio — accents, crosstalk, hold music, bad lines — with your domain vocabulary weighted so names, SKUs and policy numbers come back right first time.
Fields pulled from contracts, claims and invoices and scored per field against a human-labelled set, because one wrong field in a perfect extraction is still a rework.
Forty-plus languages with your glossary and tone enforced, and a review path for the content where a mistranslation is a legal problem rather than an awkward sentence.
Summaries that keep what the reader has to act on and are evaluated for omission as well as for fluency, which is the failure mode that actually costs you.
Precision tuned against your own cost of a false positive, since a filter that blocks one genuine customer is usually worse for you than ten pieces of spam getting through.
Routing and first-line answers with a confidence threshold below which the query goes to a person, and containment measured weekly rather than claimed at launch.
Tone and escalation risk measured per segment, because an overall accuracy figure reliably hides the group whose complaints are being read as neutral.
Eight stages, each ending in something you can open rather than a status update. The decision to continue is taken at the end of every one of them, and stopping is a priced outcome rather than a failure — the expensive mistake is the pilot nobody will kill after it has missed its numbers twice.
We start from the decision you are trying to make, not a feature list. The regulatory surface, the integration nobody owns and the evaluation set that defines success are all named in week one.
The unglamorous half, and the half that decides whether the rest holds up. Labelling, leakage checks, and a hard look at whether every feature will exist at inference time.
Two or three candidates benchmarked against your task rather than a public leaderboard, with cost per task tracked as closely as accuracy. The cheaper model often wins.
The product around the model: ingestion, review queue, audit trail and the integrations into whatever you already run. Built in the same sprint as the inference, not after it.
Measured against the set we agreed in stage one, which is the point of agreeing it before anyone had a result to defend. Per-field and per-segment, never just an average.
Staged rollout behind a flag, with the rollback tested before the release rather than during the incident. Residency and retention settled long before this point.
Drift detection, cost tracking and a monitored quality budget, plus a monthly note on what moved — written for the person who has to repeat it, not for another engineer.
New labelled examples fed back on a schedule, so accuracy climbs rather than decays. You can take this over yourself: the pipelines and evaluation sets are yours throughout.
What an NLP feature has to do before it is worth shipping to the people who will rely on it every day.
Every response cites the document it came from, or declines. An assistant that cannot show its source is one nobody in a regulated business is allowed to act on.
Scores that are calibrated rather than merely high, so a threshold means something and the review queue gets the cases that genuinely need a person.
Forty-plus languages with your glossary and tone enforced, and a review path for the content where a mistranslation is a legal exposure rather than an awkward sentence.
Every run keeps what it read, what it scored and what it changed, so a regulator's question is answered by replaying it rather than reconstructing it months later.
Bring us the corpus, the question you want answered 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 search would do this for a tenth of the price, you get that instead.
Talk to an NLP EngineerWhat you get from an engagement beyond the model itself. Each of the six below is something you can check rather than take on trust, which is the only kind of claim worth making at this stage of a sales conversation.
Ingestion, labelling and lineage treated as the product they are. Most model failures we are called in to fix are data failures, and most were visible in week one.
Outputs shaped around the decision somebody has to take, with the confidence and the source attached, rather than a score that leaves the judgement exactly where it was.
Tone, intent and churn signal read from the channels your customers actually use, measured per segment so the group you are failing does not hide inside an average.
The dull, high-volume paths first, instrumented before any of them are replaced, so the saving is measured against a real baseline rather than an estimate in a proposal.
Messaging adapted to segment and history with the guardrails written first, because personalization that gets a name or a policy number wrong costs more than generic copy.
Forty-plus languages with your glossary enforced, and a human review path for the content where being approximately right is not good enough.
Six reasons that hold up after the first release, which is the only point at which any of them matter. Ask any supplier for the labelled set they agreed before training, the engagement where they recommended not building, and the monthly running cost of their last deployed model. The answers are more revealing than the case studies.
People who have taken language models to production in regulated settings and then maintained them through an SDK 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.
Tuned on your corpus, your vocabulary 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 one that lost as well as the one that won.
Cost per task tracked as closely as accuracy from the first sprint. On narrow problems the smaller, cheaper model frequently wins, and we always test for it — the model that takes the benchmark and loses the unit economics is not the one you want in production.
Residency, retention and access settled at architecture rather than at 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.
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. The retraining pipeline runs in your account and is documented well enough for your team to own it.
Throughput, latency and cost measured under load before launch rather than discovered by it. Batch and real-time paths costed separately, because the choice between them usually decides the architecture and is expensive to reverse afterwards.
We will not quote a number before seeing your data, and anyone who does is guessing. What we will do is agree a labelled evaluation set in the first fortnight and report against it per field and per segment, so accuracy is something you can check rather than something we assert.
Almost always, and the first fortnight is spent finding out what state they are really in. Scanned PDFs, inconsistent templates and mis-OCR'd text are the normal case rather than the exception, and handling them properly is most of what separates a working system from a demo.
Forty-plus out of the box, with your glossary and tone enforced. For the languages where quality matters most to you we build a review path rather than trusting the model, because a mistranslation in a policy document is a legal exposure, not a typo.
Residency, retention and access are settled at architecture rather than at security review. Your data is excluded from third-party model training by configuration and by contract, and every decision keeps what it read and what it changed so a run can be replayed.
Then we say so, and in roughly one engagement in five we do. A well-indexed search over documents you already hold solves more business problems than a custom model does, at a fraction of the cost. That recommendation is part of the consulting engagement, not an upsell we skip.
