EGGKNITE is a AI & machine learning agency working with Austin brands as a senior, remote team. We help the Austin metro companies put predictive models into production — without the overhead of a local retainer shop.
Austin pairs a fast-growing SaaS and hardware scene with a strong consumer-brand base. Capital is disciplined here, so growth has to show a defensible number rather than a vanity chart.
Austin pairs a fast-growing SaaS and hardware scene with a strong consumer-brand base. Capital is disciplined here, so growth has to show a defensible number rather than a vanity chart. That is the context we build AI & machine learning for.
We run as a distributed senior team, not a Austin storefront — so your budget buys operators and engineering instead of a downtown office. We work inside your own accounts, on CT hours, and most clients never needed us in the room to hit their numbers.
A model that lives in a slide deck has never changed a single decision. Artificial intelligence only counts when it runs in production, changes what your team does next, and shows up in revenue you can measure against a clear baseline. Everything else is expensive theater.
Scoring, churn, LTV, segments, and recommendations built to convert. We train models on your closed-won history and customer behavior: lead scoring, churn propensity, lifetime value, and demand forecasting, plus catalog-trained recommendations that raise order value and repeat purchase. Scores retrain as your funnel shifts and land in your CRM views.
Your own language models, grounded in your data, running in your cloud. We deploy internal LLMs and retrieval pipelines over your documents, knowledge base, and warehouse. Your data stays inside your environment, behind your access controls, powering search, drafting, and answers your team can trust.
Sentiment, transcription, and classification across text, media, and calls. We build pipelines that classify tickets, reviews, calls, and leads, score sentiment, and route what matters to the right owner. Speech-to-text, video understanding, and image analysis turn media libraries and feedback into searchable, reportable data you can act against.
If it is a data problem attached to revenue, we have built it. Fraud detection, pricing and promotion optimization, anomaly detection: we scope the use case and backtest it against your own history. We tell you before you pay whether the data can carry a model that beats what you do today.
Austin's market over-indexes on B2B SaaS, hardware and consumer brands. The AI & machine learning playbook is not identical across them, so we tailor the audiences, offers and measurement to the sector you actually sell into.
We inventory your data, systems, and current decision-making, then rank candidate AI use cases by revenue at stake and data readiness, whether that is a predictive model, an internal LLM, or a language or vision pipeline. You end the month with a scored roadmap, a named first build, and an honest read on any foundation work it needs. We commit to the highest-value problem before writing a line of code.
We assemble clean training and grounding data, build the first solution, and validate it against your own history and real workloads to prove it beats your current approach. You see the expected lift and the baseline it cleared before anything goes near production. If the data cannot support the build, we tell you immediately and adjust the plan.
The first build deploys into your stack, and its output flows into the CRM, ad platforms, or internal tools where decisions get made. A clear baseline goes live so we can measure real incremental impact. You finish the quarter with AI in production, a first read on impact, and the next build already scoped.
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