Softment

AI Development

NLP / Text Analytics Development

We build NLP and text analytics features that turn messy text into structured signals—classification, extraction, summarisation, and search—integrated into your product with measurable quality.

TimelineTypical: 3–8 weeks (scope-dependent)
Starting at€1.8k
Security-first AI integrations • Evals + logging + guardrails included

Overview

What this service is

This service designs a text analytics workflow: input sources, taxonomy, model approach (LLM or classic), and an implementation that handles real-world text variability.

We implement pipelines for batch or real-time processing, integrate outputs into your systems, and build evaluation so quality is visible and improvable.

You get a maintainable setup with monitoring and guidance on improving prompts/models and expanding coverage over time.

Benefits

What you get

Automate manual triage work

Classify and route tickets, requests, or messages without human bottlenecks.

Extract structured fields reliably

Turn free-text into JSON fields your systems can use for workflows and reporting.

Better search and discovery

Improve search relevance and content grouping with embeddings and text signals.

Quality you can measure

Evaluation harness so improvements are tracked instead of subjective.

Flexible model strategy

Choose LLM or lighter models based on cost, latency, and accuracy needs.

Production-ready integration

Pipelines and APIs designed to run reliably in real workloads.

Features

What we deliver

Taxonomy + labeling strategy

Define categories, entities, and fields—and plan data labeling or prompt strategy.

Classification and routing

Models or prompt workflows for classifying text into meaningful operational categories.

Entity and field extraction

Extract structured fields with validation and confidence handling for production use.

Summarisation and insights

Generate concise summaries, key points, and action items aligned to your team’s needs.

Embedding-based search (optional)

Semantic search and clustering for better discovery and grouping of unstructured content.

Evaluation + monitoring

Quality tracking, drift checks, and monitoring hooks so the system improves over time.

Process

How we work

1
3–5 days

Discovery

We review sample text, define outcomes, and choose an approach based on constraints and accuracy needs.

2
3–7 days

Design

We define taxonomy, extraction fields, and evaluation approach before building pipelines.

3
2–6 weeks

Build

We implement classification/extraction pipelines and integrate outputs into your systems.

4
1–2 weeks

Evaluation

We test against representative cases, measure quality, and iterate until results are stable.

5
2–4 days

Launch + Handoff

We ship monitoring and documentation so the system can improve and expand over time.

Tech Stack

Technologies we use

Core

OpenAI API (optional)Transformers / spaCy (optional)EmbeddingsVector DB (pgvector)

Tools

Batch + streaming pipelinesTypeScript / Node APIsPython (optional)Evaluation harness

Services

Data validationMonitoring/logging

Use Cases

Who this is for

Support ticket classification

Auto-label and route tickets to the right queue with summaries for faster response.

CRM enrichment from emails

Extract contacts, intents, and key details from inbound communication into structured CRM fields.

Compliance text processing

Flag policy violations and extract evidence from documents and conversations.

Document summarisation workflows

Summarise contracts, proposals, and reports into structured brief outputs.

Semantic search for knowledge bases

Improve discovery of relevant docs and topics with embedding-based search patterns.

FAQ

Frequently asked questions

It depends on accuracy, latency, and cost constraints. We often start with LLM-based extraction and evaluate whether lighter models can meet requirements at scale.

Yes. We can scope privacy constraints and implement access control and data handling rules aligned to your requirements.

Yes. We build an evaluation harness so quality is measurable and improvements are tracked.

Yes. We can support real-time APIs or batch pipelines depending on volume and latency needs.

Yes. We integrate results into CRMs, ticketing, dashboards, or data stores using APIs and webhooks.

Regional

Delivery considerations for your region

Compliance & Data (EU)

For Germany/EU delivery, we keep GDPR-first patterns: data minimisation, purpose-limited storage, and explicit access boundaries.

We can work under a DPA (template available on request) and implement pragmatic retention/deletion flows when needed.

  • GDPR-first architecture patterns (generic, no legal claims)
  • DPA template available on request
  • Retention/deletion and export flows where required
  • Least-privilege access and safe logging defaults
  • Documented data flows and access boundaries

Timezone & Collaboration (EU)

We align to EU working hours with CET-friendly collaboration windows and async progress updates.

We keep delivery predictable: weekly milestones, documented decisions, and clear scope control.

  • EU overlap with CET-friendly windows
  • Async-first delivery with written decisions
  • Weekly milestone demos and progress checkpoints
  • Clear change control to avoid surprises
  • Escalation path for blockers and risks

Engagement & Procurement (EU)

We support procurement-friendly engagements with clear scopes, milestone plans, and documentation that stakeholders can review.

For EU teams, we can structure invoices and milestones for EUR-based engagements where appropriate.

  • EUR-based engagements and invoicing options
  • Discovery-first option to reduce delivery risk
  • Milestone-based billing and scope sign-offs
  • Vendor onboarding documentation on request
  • Transparent change control and approvals

Security & Quality (EU)

We prioritise reliability: reviewable PRs, predictable releases, and tests that protect critical paths.

Performance budgets and clear release discipline keep the product stable as it grows.

  • CI-friendly testing: unit + integration + smoke tests
  • Performance budgets + bundle checks
  • Release checklist + rollback-safe deployments
  • Security checklist for auth and sensitive data flows
  • Observability hooks (logs + error tracking) ready for production
Ready to start?

Need insights from text at scale?

Share sample data and outcomes you want (labels, fields, summaries). We’ll propose an NLP approach and delivery plan.

Evaluation + integration guidance included.