Bridge the deployment gap. We turn experimental algorithms into revenue-generating assets with Sovereign AI architectures and rigorous MLOps.
Engineering Standards compliant with
91% of enterprises invest in AI, yet nearly 30% of GenAI projects fail after the Proof of Concept phase due to infrastructure complexity and governance gaps.
"The challenge isn't building the model. It's building the system that keeps the model running, secure, and legally compliant 24/7."
We don't just write code; we architect systems. Our modular approach ensures every component of your AI stack is robust, scalable, and secure.
Deploy Large Language Models (LLMs) on private, air-gapped infrastructure. Complete data residency and compliance without reliance on public APIs.
Adapt smaller, efficient models (Mistral, Llama 3) to your enterprise domain using Retrieval-Augmented Generation for high-accuracy, hallucination-free outputs.
Unify unstructured text, logs, and vector embeddings into a cohesive data architecture optimized for high-throughput AI inference.
Build autonomous agents that plan, reason, and execute complex workflows—moving from "Chatbots" to "Action-Bots" that perform real tasks.
Reduce OpEx with quantization and model pruning. We ensure your model costs don't scale linearly with your user base.
Automated pipelines for drift detection and retraining. We solve the "Day 2" problem of model decay and prompt injection vulnerability.
System Status
Stop the "Token Burn." We optimize inference to keep operational costs low, utilizing open-source models (Llama 3, Mistral) where possible to avoid vendor lock-in capabilities.
Your Data, Your Perimeter. We implement Zero Trust architectures ensuring no sensitive IP leaks to public "World Models" or shared GPU clusters.
Leverage our pre-built "Productionization Frameworks" to accelerate time-to-market by 1.5x, bypassing common validation bottlenecks.
Our "Human-in-the-Loop" (HITL) workflows and automated observability tools reduce alert noise and keep model accuracy high over time.
Feasibility analysis and Economic Viability Assessment. We define the KPI before writing the code.
Feasibility Report & KPI Definition
Data Lakehouse construction, feature engineering, and selecting the right model (Proprietary vs. Open Source).
Production-Ready Model & Pipeline
Containerization (Docker/Kubernetes) and API integration. The model goes live in a scalable environment.
Scalable API & Container Registry
Continuous loop of feedback, drift monitoring, and automated re-training triggers.
Automated Retraining Loop
Need velocity? We build MVP-centric solutions focusing on specific use cases (e.g., Anomaly Detection) with pragmatism and cost-efficiency.
Build an MVPNeed governance? We deliver "Total Reinvention" with sovereign data controls, rigid compliance adherence, and legacy system integration.
Scale Your InfrastructureWe use a "Sovereign AI" approach. We deploy models within your private cloud environment (secure enclaves), so data never leaves your control. To combat hallucinations, we implement RAG (Retrieval-Augmented Generation) grounded in your specific documents and use "Explainable AI" libraries.
AI is not just code; it's infrastructure. Freelancers build models; we build deployments. We provide the MLOps, security governance, and data engineering required to keep a model running 24/7 without crashing.
Yes. We specialize in Model Distillation and SLMs. We can often replace a generalist GPT-4 call with a fine-tuned, smaller model (like Mistral 7B) that runs at a fraction of the cost for specific tasks.
Absolutely. AI models degrade (drift) over time. Our engagement includes setting up Observability Pipelines (using tools like MLflow or Arize) to monitor performance and trigger retraining when necessary.
Using our pre-configured infrastructure templates, we can typically move from Discovery to a functioning MVP in 4-6 weeks, depending on data readiness.
Stop building isolated models and start engineering measurable business value. Scale your data maturity with Prism's high-performance architectures.
