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 atCA$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 (Canada)

For Canadian teams, we focus on practical privacy and security: least-privilege access, clear boundaries, and reviewable operational controls.

We can align implementation with SOC 2 / ISO-friendly practices (without claiming certification) and support documented data flows.

  • SOC 2 / ISO-friendly patterns (no certification claims)
  • Least-privilege access and secure session handling
  • Retention/deletion and export flows where required
  • PII-safe logging + access boundary documentation
  • NDA and vendor onboarding docs on request

Timezone & Collaboration (North America)

We work with Canadian teams with North America overlap and meeting windows that fit your schedule.

Delivery stays predictable via weekly milestones, async updates, and clearly documented decisions.

  • North America overlap and responsive communication
  • Async-first updates with written scope decisions
  • Weekly milestone demos and progress checkpoints
  • Clear escalation path for blockers
  • Tight change control with clear sign-offs

Engagement & Procurement (Canada)

We support procurement-friendly delivery: clear scope, change control, and billing cadence aligned to milestones when appropriate.

We can invoice in CAD for CAD-based engagements where required.

  • CAD-based engagements and invoicing options
  • Milestone-based billing and scope sign-offs
  • Time-and-materials for evolving requirements
  • Vendor onboarding pack on request
  • Optional paid discovery to de-risk delivery

Security & Quality (North America)

We keep quality visible: clean PRs, reviewable changes, and test coverage that matches the risk of each feature.

Performance budgets and release discipline help maintain stability as the product scales.

  • CI-friendly testing: unit + integration + smoke tests
  • Performance budgets + bundle checks
  • Structured release notes + rollback-safe deployments
  • Security checklist for auth, roles, and 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.