Service · AI
Turn 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.
From prose to structured signal
UNSTRUCTURED TEXT
Validated on your data · deployed in your cloud
The real problem
Why most of an enterprise's text never gets used.
Because it's locked in prose — tickets, contracts, claims, reviews, clinical notes, transcripts — and a person has to read each one to get the answer out.
The hard part isn't the model; it's the engineering around it: picking the right technique, proving accuracy on your real text before it's trusted, redacting sensitive data, wiring output into the system that acts on it, and monitoring as your language drifts.
Of knowledge workers' time goes to searching and processing documents — the manual reading NLP is built to remove.
Of NLP use cases projected to run on foundation models by 2027 — up from under 5% in 2021. The technique landscape is shifting fast.
Where it pays off
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, complaints — by topic, urgency, or department so it lands in the right queue automatically.
Faster routing, sorting hours reclaimed
Entity & information extraction
Pulls specific fields — names, dates, amounts, clauses, codes — out of documents, turning prose into structured data.
Document-to-data in seconds, not minutes
Sentiment & intent analysis
Scores tone and intent across reviews, surveys, and transcripts at a scale no team can read.
Free-text responses become a number you track
Summarization
Condenses long material — transcripts, filings, ticket threads — into a faithful short form, source kept for verification.
Act on the gist in minutes, not hours
Semantic search & Q&A
Staff search your documents by meaning, not keywords, and get the passage that answers the question.
The right answer in one search
Translation & multilingual
Translates text and runs the same classification, extraction, and search across every language you operate in.
One workflow serves every market
PII detection & redaction
Masks names, account numbers, and health details before text is stored, shared, or fed to another system.
Text put to work without exposing regulated data
As of June 2026 · revisit quarterly
What NLP does to those processes — the measured impact.
Independent industry findings, cited as third-party evidence — never Silicon Prime's own client results.
Document work collapses. Faster turnaround when document-heavy workflows are automated, with processing costs cut ~40% — the core NLP payoff.
Agents resolve more. More issues resolved per hour from gen-AI assist in a study of ~5,000 agents, with handle time cut ~9%.
The field is shifting. Of NLP use cases projected to run on foundation models by 2027 — up from under 5% in 2021, so the technique choice matters more than ever.
No model ships unless it beats the baseline. Measured on a production-realistic split, on the metric that matches the business cost — not headline accuracy on a slide.
What's included
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. The honest "not worth building" call is 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 production accuracy.
Model development — classical & LLM
We build classification, extraction, sentiment, summarization, search, and translation models, choosing the architecture on your constraints — latency, cost, interpretability, data sensitivity.
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. 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.
What you get — all assigned to you under full work-for-hire IP
How it runs
How an NLP engagement runs.
The same delivery model behind all our AI development work — one accountable lead, fixed scope, no handoffs.
STEP 01
Frame
Define the language task, the data available, and the metric and baseline the model must beat.
Output: a ranked plan & the success criteria
STEP 02
Build
Design the labeling scheme, annotate, and develop and compare candidate models — classical and LLM-based — in your cloud.
Output: a candidate model & a documented comparison
STEP 03
Validate
Measure against the baseline on a production-realistic split, check the costly edge cases, and confirm the redaction holds.
Output: an evaluation report & a go/no-go
STEP 04
Deploy & enable
Ship as a monitored service wired into your workflow, instrument it for drift, and train your team to read the dashboards and retrain it.
Output: a production system & a team that owns it
Straight talk
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.
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.
Silicon Prime is a Stanford-rooted Responsible AI lab, founded 2011, run by founder Kelvin Tran — 20+ years of production engineering, personally accountable for every engagement.
Why build your NLP with us.
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.
Where it lands first
Where NLP earns its keep first.
Healthcare
Clinical-note summarization, document extraction, and de-identification inside HIPAA-compliant architectures, every PII path auditable.
Healthcare software →Fintech
Contract extraction, complaint classification, and adverse-media screening, every model conservative and traceable for the audit.
Fintech software →Ecommerce & retail
Review and survey sentiment, product-attribute extraction, and semantic search over the catalog, measured against the baseline it has to beat.
Ecommerce software →Legal & operations
Clause extraction, document classification, and summarization of long filings, with the source kept so a person verifies rather than trusts.
Operations platforms →Questions buyers ask before they build.
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.