Natural language processing services
Turn your unstructured text into signal you can act on.
NLP that reads the text your business generates — classify, extract, score, summarize, search, translate — validated against your real data and deployed in your own cloud, in 4–8 weeks.
Where NLP actually pays — and what each capability delivers
NLP isn’t one product; it’s a toolkit, each tool earning its keep on a specific high-volume language task.
Text classification & routing
Labels inbound text — tickets, emails, forms, complaints — by topic, urgency, or department so it lands in the right queue automatically. Benefit — faster routing and consistent triage, with manual sorting hours reclaimed.
Entity & information extraction (NER)
Pulls specific fields — names, dates, amounts, clauses, product codes, lab values — out of unstructured documents and turns prose into structured data. Benefit — document-to-data turnaround drops from minutes-per-file to seconds, with a steadier error rate.
Sentiment & intent analysis
Scores tone and intent across reviews, surveys, transcripts, and social mentions at a scale no team can read. Benefit — the signal in thousands of free-text responses becomes a number you can track.
Summarization
Condenses long material — transcripts, filings, research, ticket threads — into a faithful short form, with the source kept for verification. Benefit — reading time on long documents collapses, and people act on the gist in minutes.
Semantic search & question answering
Lets staff search your documents by meaning, not keywords, and returns the passage that answers the question. Benefit — the right answer found in one search instead of a hunt across systems.
Machine translation & multilingual processing
Translates text and runs the same classification, extraction, and search across the languages your customers and staff use. Benefit — one workflow serves every market, without a separate team per language.
PII detection & redaction
Finds and masks names, account numbers, health details, and other sensitive data before text is stored, shared, or fed to another system. Benefit — text can be put to work without exposing regulated data.
What our NLP development covers
The difference between an NLP system that runs the business and a notebook that scores well on a slide.
Use-case scoping & technique selection
We decide what to build and, crucially, how — a fast, auditable classical model where it wins, a foundation model where the task demands flexibility. Run as our AI readiness assessment, with the honest “not worth building” call included.
Data, labeling & annotation
We assess your text, design the labeling scheme, and build the annotated set the model learns and is judged against — handling the class imbalance and edge cases that quietly wreck accuracy in production.
Model development — classical and LLM-based
We build classification, extraction, sentiment, summarization, search, and translation models, choosing the architecture on your constraints — latency, cost, interpretability, data sensitivity. Where a large language model is the right call, that’s our generative AI and LLM development work; where a leaner model wins, we build that.
Honest evaluation & validation
Every model is measured against a baseline on a production-realistic split, on the metric that matches the business cost — precision/recall, field-level accuracy, faithfulness — never just headline accuracy. A model that doesn’t beat the baseline doesn’t ship.
Privacy, redaction & governance
We build PII detection and redaction into the pipeline, document every data path, and favor approaches your risk team can audit — so sensitive language is protected before it’s stored, shared, or used to train anything.
Deployment, integration & enablement
We ship the model as a monitored service in your own cloud, wired into the system that acts on its output, instrumented for accuracy drift, and handed over with the retraining path and a trained team in place.
What you get when you hire us — all assigned to you under full work-for-hire IP
- A trained, validated NLP system in your own cloud tenant
- The labeled dataset and annotation guidelines
- The evaluation suite and baseline
- The redaction and governance layer
- Monitoring and drift dashboards
- Runbooks and a trained team
The production discipline behind a text system you’d actually trust
We’re candid: an NLP system is only as trustworthy as the engineering underneath it, and we don’t claim a published case study for every capability above. What we can show is a track record of taking real software from prototype to dependable production and operating it for years.
The clearest evidence is Bridge Athletic: a partnership since 2012 we carried from a day-one build through more than a decade of re-engineering — never going offline — into a platform now used by USC, the LA Rams, and MLB and MLS teams. Operating a data-driven system reliably across 12+ years is the same muscle an NLP pipeline needs — validate before you ship, monitor after — the discipline that runs through our Aegis AI delivery process.
Silicon Prime is a Stanford-rooted Responsible AI lab, founded in 2011, run by founder Kelvin Tran — 20+ years of production engineering, personally accountable for every engagement. We’ll tell you plainly when NLP is the wrong tool, or when a keyword rule beats a model — which a vendor paid to ship one won’t.
Why build your NLP with us
A record of shipping software that survives in production, not a portfolio of demos.
The right tool, not the trendy one. A lean classical model when it’s faster and more auditable, a foundation model only when the task needs it — we’re not paid to sell you the expensive option.
Honest evaluation is non-negotiable. A model that doesn’t beat its baseline on a production-realistic split doesn’t ship.
Responsible AI is the founding charter. Redaction, audit trails, and governance are part of the build, not an afterthought — which matters most where the text is regulated.
Founder-led, one accountable lead. No account managers, no handoffs — the person who scopes the work answers for it.
Built to transfer. Models, datasets, evals, and code assigned to you under full work-for-hire IP, your team trained to retrain and extend them. You own the asset, not a dependency.
Questions buyers ask before they build
How is NLP development different from your LLM, generative, and conversational AI work?+
NLP is the language toolkit — classification, entity extraction, sentiment, summarization, semantic search, translation, and redaction — applied to your text with whichever technique fits, classical or LLM. Generating new content is generative AI development; a chat or voice assistant is conversational AI; prediction on structured or image data is machine learning development. Many systems combine several, so we scope which your problem needs.
Do you use classical NLP or large language models?+
Whichever wins. A foundation model is flexible but heavier, slower, and harder to audit; a fine-tuned classical model is often faster, cheaper, and easier to govern for well-defined jobs like routing or extraction. Gartner projects foundation models will underpin 60% of NLP use cases by 2027, but “most” isn’t “all” — we benchmark both on your data and recommend on evidence.
Do we have enough data, and labeled correctly?+
Often yes, and we give the honest answer early. The first phase assesses your text volume and quality and designs the labeling scheme — in NLP, annotation guidelines and inter-annotator agreement decide accuracy as much as the model does. Where labeled data is thin, modern foundation models can do useful work with few or no examples, and we’ll tell you when that fits.
How do you know it actually works before we deploy it?+
We measure it against a baseline on a held-out split that reflects production, on the metric matching the business cost — precision and recall for classification, field-level accuracy for extraction, faithfulness for summaries — not just headline accuracy. A model that doesn’t beat the baseline doesn’t ship. Then we monitor for accuracy drift, because language shifts and a model can quietly go wrong.
How do you handle sensitive text and PII?+
Redaction is built into the pipeline: we detect and mask names, account numbers, health details, and other sensitive data before text is stored, shared, or used for training. Models run in your own cloud tenant under your access controls, every engagement starts with an NDA and a security review, and we document every data path — which matters most in fintech and healthcare.
Who owns the models and the code when you’re done?+
You do — completely. The trained models, the labeled datasets and annotation guidelines, the evaluation suites, and all code transfer under full work-for-hire IP assignment signed at kickoff, and your team is trained to retrain and extend them. The engagement is built around the handover, not around locking you in.
What do natural language processing services cost and how long do they take?+
Most NLP systems reach production in 4–8 weeks under a fixed-scope engagement with one accountable lead, and payment is tied to the agreed ROI. Build cost depends on scope and data readiness — our AI development cost guide gives real ranges — and we model the ongoing serving and retraining cost before building, so the running cost is a forecast you’ve already seen.
Thirty minutes · No pitch deck
Ready to turn your text into something you can act on?
Bring the text you’re drowning in — tickets, documents, reviews — and we’ll tell you honestly whether NLP fits, which technique to use, and what it costs to run.