NLP Development Services

NLP development for text decisions that need labels, not fluent prose.

We build text classification, entity extraction, relationship detection, scoring, and routing systems for a defined corpus, language, and taxonomy. A focused release includes labelled examples, per-class evaluation, confidence thresholds, human review, integration, monitoring, and a retraining plan.

See our work

Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.

The brief

Start with what is not working.

Good software decisions begin with the constraint, not a list of features or a preferred technology.

01

Are teams reading and tagging the same high-volume text while category definitions change between reviewers?

02

Does a plausible generative answer hide which label, entity, evidence, or confidence the downstream workflow needs?

Plain answer

NLP development turns text into governed labels, entities, relationships, scores, or routes. RaftLabs builds it when a product needs repeatable classification or extraction at scale and generative prose is the wrong output. A focused system for one corpus, language, and taxonomy starts at $25,000 and usually takes 10 to 14 weeks.

The model sounded certain. The routing label was still wrong.

Three reviewers used different meanings for urgent, cancellation risk, and billing issue. A generative model returned polished explanations, but the queue needed one traceable category and a safe fallback. The first product problem was agreement on the taxonomy. Model choice came later.

Focused delivery baseline

starting first release
$25K
One corpus, language, and taxonomy
typical focused timeline
10-14 weeks
Includes labelling and controlled release
evaluation level
Per class
Avoid one flattering aggregate score

RaftLabs does not currently publish a named NLP classification outcome case. These figures describe delivery scope, not promised model quality. Acceptance should report precision, recall, coverage, abstention, class balance, reviewer agreement, human corrections, latency, cost, drift, and downstream outcomes for the agreed corpus.

Build a custom NLP system when recurring text must become governed structure.

Use ordinary rules or an existing product when the vocabulary is stable and the decision is common. Use LLM integration when the main outcome is generated language or contextual assistance.

A fit
01

A repeatable stream of text feeds a classification, extraction, scoring, search, compliance, or routing decision.

02

Domain owners can define labels or entities, resolve disagreement, and provide representative examples.

03

The workflow needs thresholds, abstention, review, traceability, monitoring, and controlled updates.

Not a fit
01

The team wants a general chatbot, writing assistant, broad semantic search, or open-ended summarisation feature.

02

There is no agreed taxonomy, source corpus, downstream decision, evaluation owner, or review path.

03

A few explicit keywords or a standard platform feature already solves the job at acceptable quality and cost.

Choose the service by the output the workflow needs

NeedBest fitPrimary evaluation
Labels, entities, relationships, scores, or routes from textNLP developmentPer-class precision, recall, coverage, abstention, and correction
Generated answers, summaries, drafting, or model-assisted product featuresLLM integrationTask rubric, groundedness, safety, latency, cost, and user outcome
Classify, extract, validate, and route business documents end to endIntelligent document processingDocument and field accuracy, exception rate, and workflow completion
Recognise objects, scenes, movement, or defects in images or videoComputer visionClass, object, event, or spatial measures on representative media

Scope

What belongs in a focused NLP system

  • 01

    Taxonomy and annotation guide

    Define labels or entities with examples, exclusions, precedence, ambiguity rules, required evidence, sensitive fields, and a process for resolving reviewer disagreement.
  • 02

    Representative evaluation set

    Sample normal language, shorthand, misspellings, long tails, rare but costly cases, class imbalance, and temporal variation. Keep a protected set for honest comparison.
  • 03

    Model, rules, or hybrid pipeline

    Benchmark the smallest viable approach. Combine deterministic rules and learned models when that improves control, then return a versioned structured output rather than free-form prose.
  • 04

    Confidence and human review

    Set thresholds by label and risk. Abstain or route uncertain cases, show source evidence, capture corrected decisions, and prevent a low-confidence output from triggering a sensitive change.
  • 05

    Integration and monitoring

    Connect one source and destination, preserve identifiers, trace model and taxonomy versions, monitor distribution and corrections, alert on drift, and govern retraining or prompt changes.

How it works

From text corpus to monitored structured output

  1. Phase 1
    01

    Define taxonomy and business error

    Choose the corpus, language, labels or entities, annotation rules, downstream decision, evidence, costly mistakes, abstention behaviour, owners, and acceptance measures.

  2. Phase 2
    02

    Label and benchmark representative text

    Sample real language and edge cases, resolve reviewer disagreement, protect sensitive data, split evaluation sets, test rules and model baselines, and expose weak classes.

  3. Phase 3
    03

    Build classification and review flow

    Implement preprocessing, model or hybrid logic, confidence thresholds, structured output, evidence, human review, integration, access controls, versioning, and traceable corrections.

  4. Phase 4
    04

    Release, monitor, and retrain

    Run shadow or limited traffic, measure per-class quality and coverage, inspect drift and corrections, tune thresholds, document limits, train owners, and schedule governed updates.

Risk

What the text system must make visible

Taxonomy disagreement
Measure reviewer agreement and record adjudication. A model cannot produce a stable label when domain owners use the label differently.
Weak rare classes
Report each consequential class separately. Balanced sampling, thresholds, and human review matter more than a high aggregate score.
Language and context drift
Monitor input and output distributions, new vocabulary, channel changes, seasonal patterns, and correction rates by model and taxonomy version.
Sensitive text
Minimise collection and retention, control access, redact where appropriate, document provider processing, and keep regulated decisions under accountable review.

Scope and price

A focused NLP release starts at $25,000.

Start with one corpus, one language, one taxonomy, representative labelled cases, one downstream integration, a review queue, and an owner for corrections.

A broader text-intelligence programme commonly reaches $50,000 to $110,000. If keywords, rules, or an existing product meet the threshold, use them.

Starting investment

Starts at $25,000

A focused release usually takes 10 to 14 weeks. Additional languages, label sets, channels, complex annotation, sensitive data, or high-assurance decisions add time.

Scores stay tied to a decision

We report quality by consequential class and threshold, including abstentions and human corrections, rather than presenting one aggregate number as the answer.

The model can decline

Low-confidence or out-of-scope text follows a defined review or safe-default path instead of forcing a confident label.

Related AI and document services

Common questions

NLP is useful when recurring text must become consistent labels, entities, relationships, scores, or routes. Examples include support triage, topic tagging, feedback coding, clause or record extraction, and risk signals. The work should have a defined downstream decision, enough representative text, and people who can resolve ambiguous labels.

NLP here produces constrained, testable structure from text. LLM integration is broader and may generate, summarise, search, reason over context, or use tools. An LLM can be one implementation option inside an NLP system, but the output contract, labelled evaluation, thresholds, review, and monitoring still define the product.

There is no responsible fixed number. It depends on label count, class balance, language variety, ambiguity, error cost, and the chosen approach. We start with a representative sample, measure reviewer agreement and baseline quality, then estimate the next useful labelling tranche instead of assuming a large dataset.

Set thresholds by class and business risk. The system can abstain, ask for missing information, route a case to review, or apply a safe default. Corrections should retain the original text, model and taxonomy version, output, reviewer decision, and reason so monitoring and retraining use trustworthy evidence.

A focused release starts at $25,000 and usually takes 10 to 14 weeks. It covers one corpus, language, taxonomy, labelled evaluation set, model or hybrid baseline, review flow, one integration, monitoring, and handover. More languages, labels, sources, sensitive data, or high-assurance decisions increase scope.

Work with us

Bring the text, the taxonomy, and the decision it should support.

Share representative examples, languages, current labels, reviewer disagreement, downstream actions, error costs, privacy constraints, and the team that will own corrections.

  • Scope and cost agreed before work starts. No surprises. No obligation.
  • Working prototype within 3 weeks of kickoff.
  • Pay by milestone. You see progress before each invoice.
  • 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
  • All conversations are NDA-protected.