Turn your unstructured text into signal you can act on.
We build the NLP that reads the text your business generates and makes it usable — classify and route inbound messages, extract the fields buried in documents, score sentiment, summarize, power semantic search, and translate.
The right tool for the job: a fast classical model where that wins, a foundation model where flexibility matters. Validated against your real data, deployed in your own cloud. Fixed scope, one accountable lead, production in 4–8 weeks.
Because it’s locked in prose. Support tickets, contracts, claims, reviews, emails, clinical notes, call transcripts — the answers your business needs are in there, but a person has to read each one to get them out. So the work gets done by hand, slowly and inconsistently, or it doesn’t get done at all and the signal is simply lost.
The hard part of fixing this is rarely the model — modern NLP reads language remarkably well. The hard part is the engineering around it: deciding which technique actually fits the task, building an evaluation set from your real text so you know it’s accurate before it’s trusted, redacting the sensitive data it will inevitably touch, wiring its output into the system that acts on it, and monitoring it as your language drifts.
That surrounding system is the whole job of natural language processing services, and it’s what decides whether any of your text turns into value.
NLP isn’t one product; it’s a toolkit, and each tool earns its keep on a specific, high-volume language task. For each: what it does, the benefit it produces, and a one-line illustration of the help.
Reads inbound text — tickets, emails, forms, complaints — and labels it by topic, urgency, language, or department so it lands in the right queue automatically. Benefit — faster routing and consistent triage, with manual sorting hours reclaimed.
For example, an urgent safety complaint buried in a flood of routine email is auto-flagged and pushed to the right team in seconds instead of sitting in a shared inbox until someone reads down to it.
Pulls the specific fields out of unstructured documents — names, dates, amounts, clauses, product codes, lab values — and turns prose into structured data. Benefit — document-to-data turnaround drops from minutes-per-file to seconds, with a steadier error rate.
For example, an invoice or a contract is read once and its key terms land in the system as fields, so a clerk verifies the extraction instead of re-typing the whole page.
Scores tone and intent across reviews, survey responses, support 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.
For example, a spike in negative sentiment about one product feature surfaces in a dashboard the week it starts, instead of being noticed a quarter later when churn shows up.
Condenses long material — call transcripts, filings, research, ticket threads, meeting notes — 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.
For example, an agent closing a 40-minute support call gets an accurate wrap-up note generated in seconds rather than spending five minutes writing it, so they take the next call sooner.
Lets staff search your documents by meaning, not just keywords, and returns the passage that answers the question — over policies, manuals, knowledge bases, and archives. Benefit — the right answer found in one search instead of a hunt across systems.
For example, a frontline employee asks a policy question in plain language and gets the exact clause that applies, rather than scrolling three wikis and guessing which page is current.
Translates inbound and outbound 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.
For example, a complaint written in Spanish is translated, classified, and routed through the same pipeline as an English one, so nothing waits on a bilingual reviewer.
Finds and masks the names, account numbers, health details, and other sensitive data inside free text before it’s stored, shared, or fed to another system. Benefit — text can be put to work without exposing regulated data.
For example, a batch of call transcripts is automatically redacted before it’s used to train a model or shared with a vendor, so the analysis happens without the personal data ever leaving its boundary.
The scope below is the difference between an NLP system that runs the business and a notebook that scores well on a slide.
We decide what to build and, crucially, how — a fast, cheap, auditable classical model (logistic regression, gradient boosting, a fine-tuned transformer) where it wins, or a foundation model where the task demands flexibility. Run as our AI readiness assessment, with the honest “this one isn’t worth building” call included.
We assess your text data, design the labeling scheme, and build the annotated set the model learns and is judged against — handling the class imbalance, inter-annotator disagreement, and edge cases that quietly wreck accuracy in production.
We build classification, extraction (NER), sentiment, summarization, semantic-search, and translation models, choosing the architecture on your task and your constraints — latency, cost, interpretability, data sensitivity — not on what’s fashionable. 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 instead.
Every model is measured against a real baseline on a held-out split that reflects production, on the metric that matches the business cost — precision/recall for routing, field-level accuracy for extraction, faithfulness for summaries — never just headline accuracy. A model that doesn’t beat the baseline doesn’t ship.
We build PII detection and redaction into the pipeline, document every data path, and — for regulated text — favor approaches your risk team can audit, so sensitive language is protected before it’s stored, shared, or used to train anything.
We ship the model as a monitored service in your own cloud — batch or real-time — wired into the system that acts on its output, instrumented for accuracy drift as your language changes, 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
The same delivery model behind all our AI development work, tuned for language systems — one accountable lead, fixed scope, no handoffs.
Define the language task, the data available, and the metric and baseline the model must beat.
Output: a ranked plan & the success criteria
Design the labeling scheme, annotate, and develop and compare candidate models — classical and LLM-based — in your own cloud tenant.
Output: a candidate model & a documented comparison
Measure against the baseline on a production-realistic split, check the failure modes and costly edge cases, and confirm the redaction holds.
Output: an evaluation report & a go/no-go
Ship as a monitored service wired into your workflow, instrument it for accuracy drift, and train your team to read the dashboards and retrain it.
Output: a production system & a team that owns it
Most engagements reach production in 4–8 weeks, full IP assignment signed at kickoff, payment tied to the ROI we agreed to deliver — not to billable hours.
We’re candid here: an NLP system is only as trustworthy as the engineering and monitoring underneath it, and we don’t claim a published case study for every capability listed above. What we can show is a track record of taking real software from prototype to dependable production and operating it for years — the exact discipline a language model in production demands.
The clearest evidence is Bridge Athletic: a product partnership since 2012 that we carried from a day-one startup build through more than a decade of modernization, re-engineering, and performance work — never going offline — into a platform now used by USC, the LA Rams, and MLB and MLS teams.
Operating a data-and-content-driven system reliably across 12+ years is the same muscle an NLP pipeline needs: validate before you ship, monitor after, and keep it working as the inputs shift. That same evals-before-launch, monitor-after discipline runs through our Aegis AI delivery process across every engagement.
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 for your problem, or when a simple keyword rule beats a model — which a vendor paid to ship one won’t.
What sets our natural language processing services apart is a record of shipping software that survives in production, not a portfolio of demos.
The right tool, not the trendy one. We’ll build a lean classical model when it’s faster, cheaper, and more auditable, and reach for a foundation model only when the task genuinely needs it — because 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, and we’ll say so rather than dress up an accuracy number.
Responsible AI is the founding charter. Redaction, audit trails, and governance over what a language model may infer and store 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 are assigned to you under full work-for-hire IP, and your team is trained to retrain and extend them when we step back. You own the asset, not a dependency.
Clinical-note summarization, intake and document extraction, and de-identification inside HIPAA-compliant architectures, every output logged and every PII path auditable. Healthcare software →
Document and contract extraction, complaint and communication classification, and adverse-media screening, every model conservative and traceable for the audit. Fintech software →
Review and survey sentiment, product-attribute extraction from supplier text, and semantic search over the catalog, measured against the baseline it has to beat.
Clause extraction, document classification and routing, and summarization of long filings, with the source kept so a person verifies rather than trusts.
What teams want to know before they put a language model into production.
This page is the broad language toolkit — classification, entity extraction, sentiment, summarization, semantic search, translation, and redaction — applied to your unstructured text, using whichever technique fits (often a lean classical model, sometimes a large language model). When the task is specifically generating new content, that’s generative AI development; when it’s a chat or voice assistant, that’s conversational AI; when it’s prediction on structured or image data, that’s machine learning development. Many real systems combine several; we scope which your problem actually needs.
Whichever wins on your task. 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 a well-defined job like routing or extraction. Gartner has projected 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, not fashion.
Often yes, and the honest answer comes early. The first phase assesses your text volume and quality and designs the labeling scheme — because in NLP the 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’s the right starting point.
We measure it against a baseline on a held-out split that reflects production, on the metric that matches 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 it for accuracy drift, because language shifts and a model right at launch can quietly go wrong.
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 to train anything. Models run inside your own cloud tenant under your access controls, every engagement starts with an NDA and a security review, and we document every data path so your risk and compliance teams audit rather than trust — which matters most in fintech and healthcare.
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.
Most NLP systems reach production in 4–8 weeks under a fixed-scope engagement with one accountable lead, and payment is tied to the ROI we agreed to deliver. 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
Bring the text you’re drowning in — the tickets, the documents, the reviews — and we’ll tell you honestly whether NLP fits it, which technique to use, what it takes to build, and what it costs to run.