NLP Development
FISTA Solutions builds natural language systems that are measured rather than assumed: classification, entity extraction, sentiment and intent, semantic search, and summarization — using the simplest method that meets the accuracy target, evaluated on labeled data from your own text.
- 150+
- projects delivered
- 50+
- companies served
- 99.9%
- verified uptime
- 47%
- efficiency gains
- 12+
- countries reached
What we build
What does NLP development include?
NLP work covers task definition and label schema design, data labeling with quality control, method selection across rules, classical models, and LLMs, evaluation per class, deployment with latency and cost budgets, and monitoring for drift.
- 01
Task and label schema
Categories defined so annotators agree, because inconsistent labels cap achievable accuracy.
Definition - 02
Labeling and quality control
Annotation with guidelines, agreement checks, and adjudication of disagreements.
Data - 03
Method selection
Rules, classical models, embeddings, or LLMs compared on accuracy, latency, and cost.
Method - 04
Evaluation
Per-class accuracy with confusion analysis, so weaknesses are specific rather than general.
Quality - 05
Deployment and monitoring
Serving within latency and cost budgets, with drift monitoring on input distribution and quality.
Operations
Requirements
Which requirements shape NLP development?
NLP quality is capped by label quality. Requirements cover a schema annotators can apply consistently, measured inter-annotator agreement, method selection by evidence, per-class evaluation, and monitoring as language and topics shift.
| Requirement | Why it matters | How FISTA builds to it |
|---|---|---|
| Label schema | Ambiguous categories cap accuracy. | Schema designed and tested with annotators, refined until agreement is acceptable before bulk labeling. |
| Annotation quality | Inconsistent labels teach inconsistency. | Guidelines, agreement measurement, and adjudication rather than single-pass labeling. |
| Method by evidence | LLMs are not always the right tool. | Rules and classical models evaluated alongside LLMs, since simpler methods are often cheaper and faster. |
| Per-class evaluation | Rare classes hide behind averages. | Accuracy per class with confusion analysis, and attention to minority classes that matter. |
| Language drift | Terminology and topics change. | Monitoring for input distribution shift and periodic re-evaluation against fresh labeled samples. |
Where AI fits
How should you sequence NLP development?
Define the task precisely and label consistently, then let measurement choose the method. Teams that jump to a model before settling the schema usually rebuild the schema anyway, after wasting the labeling budget.
- 01
1. Define categories precisely
Tested with annotators until they agree, because ambiguity caps everything downstream.
- 02
2. Label with quality control
Guidelines, agreement measurement, and adjudication rather than one pass by one person.
- 03
3. Compare methods
Rules, classical models, and LLMs measured on the same set for accuracy, latency, and cost.
- 04
4. Evaluate per class
Including rare but important classes that averages would otherwise hide.
- 05
5. Monitor and refresh
Periodic re-evaluation on fresh samples as language and topics shift.
Cost and timeline
How much does NLP development cost, and how long does it take?
Cost is driven by labeling volume and schema complexity; timeline by annotation and adjudication. FISTA does not quote blind: the scoping call returns a schema plan, a labeling estimate, and a method comparison approach.
Labeling is the main cost and the main quality determinant. A smaller, carefully labeled dataset with high annotator agreement usually beats a larger, noisier one.
Method comparison protects the operating budget. An LLM that costs materially more per request than a classical model for the same measured accuracy is a poor default, and only measurement reveals which case you are in.
Send the scope you have, even if it is a paragraph. You get a written brief, an architecture sketch, and a phased estimate before any commitment.
Get a scoped quoteDelivery
How does FISTA deliver an AI system?
FISTA delivers AI in four phases: a discovery sprint that defines the success metric, data readiness, and specification; a design that fixes the model strategy, retrieval, guardrails, and evaluation plan; iterative builds scored against a golden set; and a production release with tracing, dashboards, cost budgets, and a change process.
- 1
Discover and define
Use-case selection, data audit, success metrics, risk review, and a written specification with an evaluation plan.
OutputSpecification, golden set, estimate
- 2
Design the system
Model strategy, retrieval and data pipelines, guardrails, human review points, and the deployment target.
OutputArchitecture, model decision record
- 3
Build and evaluate
Two-week increments, each scored on the evaluation harness for quality, latency, and cost, demoed on real data.
OutputEval reports, working system
- 4
Release and monitor
Production deployment with tracing, quality and cost dashboards, drift alerts, runbooks, and a change process that re-runs the evals.
OutputProduction AI system with SLOs
Why FISTA
Why choose FISTA Solutions for NLP development?
FISTA designs label schemas annotators can actually apply, compares methods on measured accuracy and cost, and reports per class. Work is contracted through a US entity with full IP assignment.
NLP specifics
- The label schema is tested with annotators and refined until agreement is acceptable, before bulk labeling budget is spent.
- Rules, classical models, and LLMs are compared on the same evaluation set for accuracy, latency, and cost.
- Accuracy is reported per class with confusion analysis, including the rare classes that matter operationally.
- Drift monitoring and periodic re-evaluation keep performance honest as language and topics change.
How FISTA engineers
- Spec-Driven Development: every deliverable starts as a written specification with acceptance criteria, so scope is testable before it is built.
- AI-native delivery: engineers direct coding agents under review gates and evaluation harnesses, compressing build time without loosening verification.
- Official Anthropic partner, with production experience across Claude, OpenAI, Google, and open-weight models, chosen per workload rather than by default.
- One accountable delivery lead, weekly demos on your environment, and code in your repositories from week one.
What you get as a client
- 150+ projects delivered for 50+ companies across 12+ countries since 2017, with 99.9% verified uptime on systems we operate.
- A US entity (FISTA Solutions Inc., Wilmington, Delaware) for contracting, invoicing, and IP assignment, with an engineering center in Faisalabad, Pakistan for cost-efficient senior capacity.
- US business-hours overlap for standups and reviews; written decision logs so nothing depends on a meeting you missed.
- Flexible engagement: fixed-scope build, embedded forward deployed engineers, or a dedicated team that you can scale month to month.
Clear answers
What buyers ask before an AI build.
Straightforward guidance for evaluating scope, fit, and the next step.
01Should we use an LLM for text classification?
Sometimes. LLMs excel with little labeled data and nuanced categories; classical models are often faster and materially cheaper at volume for well-defined tasks. FISTA measures both on your data before recommending.
02How much labeled data do we need?
Less than expected with LLM-based approaches, more for classical models. Consistency matters more than volume, which is why schema design and annotator agreement come first.
03Our categories are ambiguous. Can you still build this?
Not well, until the schema is fixed. FISTA works with your team to refine categories until annotators agree, because ambiguous labels cap achievable accuracy regardless of method.
04How do you handle rare but important classes?
By reporting accuracy per class rather than averaging, targeting labeling effort at rare classes, and tuning thresholds against the operational cost of missing them.
05How long does an NLP project take?
Schema design and labeling usually dominate and take weeks; method comparison and deployment follow faster. The schema work precedes any timeline commitment.
Scoped in writing before you commit
Define the categories, then let measurement pick the method.
Bring your text and the decisions it drives. The scoping call returns a schema plan, a labeling estimate, and a method comparison.