AI Platform Development
Turn AI prototypes into production systems that scale, perform, and pay for themselves. We build the infrastructure, GenAI pipelines, and product engineering that take AI from demo to deployment — reliable, cost-efficient, and ready for real users.
AI Engineering That Ships — Not Just Experiments
Most AI projects stall between prototype and production. The model works in a notebook, but nobody built the infrastructure to serve it at scale, the monitoring to catch when it breaks, or the product around it that users actually want. Plus8Soft is the engineering team that closes that gap. We build production-grade AI platforms — training pipelines, inference systems, GenAI applications, and the observability layers that keep them reliable.
Services
01
AI Infrastructure & Platform Engineering
We build the foundational systems that make AI work at scale — model training pipelines (batch and real-time), distributed inference architectures, GPU orchestration across AWS, GCP, Azure, and on-prem clusters, ML data pipelines from ETL to feature stores, and multi-model routing systems for LLMs, computer vision, and multimodal AI. The core platform layer that takes you from "it works on my laptop" to production-grade performance.
01
We build the foundational systems that make AI work at scale — model training pipelines (batch and real-time), distributed inference architectures, GPU orchestration across AWS, GCP, Azure, and on-prem clusters, ML data pipelines from ETL to feature stores, and multi-model routing systems for LLMs, computer vision, and multimodal AI. The core platform layer that takes you from "it works on my laptop" to production-grade performance.
02
LLM & GenAI Systems
We build production-ready generative AI systems beyond the demo stage — agent-based multi-step workflows, custom copilots (internal tools and customer-facing), fine-tuning pipelines, and prompt engineering frameworks. The advanced layer that separates toys from tools: context management systems, tool-use orchestration, memory architectures for persistent agents, and evaluation pipelines that measure LLM output quality systematically. RAG systems are covered in depth on our RAG-as-a-Service page.
02
We build production-ready generative AI systems beyond the demo stage — agent-based multi-step workflows, custom copilots (internal tools and customer-facing), fine-tuning pipelines, and prompt engineering frameworks. The advanced layer that separates toys from tools: context management systems, tool-use orchestration, memory architectures for persistent agents, and evaluation pipelines that measure LLM output quality systematically. RAG systems are covered in depth on our RAG-as-a-Service page.
03
AI Product Development
We turn AI models into real products that people pay for — SaaS platforms built around AI capabilities, purpose-designed UI/UX for chat interfaces, AI dashboards, and copilot experiences, subscription and billing systems, admin panels with usage analytics, and mobile/web applications powered by AI. Full product engineering for AI companies that have strong models but need the product wrapper to reach customers.
03
We turn AI models into real products that people pay for — SaaS platforms built around AI capabilities, purpose-designed UI/UX for chat interfaces, AI dashboards, and copilot experiences, subscription and billing systems, admin panels with usage analytics, and mobile/web applications powered by AI. Full product engineering for AI companies that have strong models but need the product wrapper to reach customers.
04
AI Performance & Cost Optimization
We reduce AI infrastructure costs by 30–70% without sacrificing quality — latency optimization, model compression and distillation, smart routing between expensive and cheap models based on query complexity, inference caching strategies, token usage optimization, and batch processing for non-real-time workloads. The difference between an AI system that burns money and one that has sustainable unit economics.
04
We reduce AI infrastructure costs by 30–70% without sacrificing quality — latency optimization, model compression and distillation, smart routing between expensive and cheap models based on query complexity, inference caching strategies, token usage optimization, and batch processing for non-real-time workloads. The difference between an AI system that burns money and one that has sustainable unit economics.
05
AI Observability & Reliability
We build the monitoring and quality systems that keep AI platforms trustworthy in production — LLM output monitoring, hallucination detection, user feedback loops, A/B testing frameworks for prompts and models, logging and distributed tracing for AI pipelines, and alerting on quality degradation. The systems that make AI measurable, debuggable, and controllable — not a black box that breaks silently.
05
We build the monitoring and quality systems that keep AI platforms trustworthy in production — LLM output monitoring, hallucination detection, user feedback loops, A/B testing frameworks for prompts and models, logging and distributed tracing for AI pipelines, and alerting on quality degradation. The systems that make AI measurable, debuggable, and controllable — not a black box that breaks silently.
06
Data Engineering for AI
We build the data foundation that AI systems actually need — data labeling pipelines, cleaning and normalization workflows, vector database architecture (Pinecone, Weaviate, Qdrant, pgvector), knowledge base structuring, feature stores, and data governance frameworks. AI is only as good as its data — we make sure the data layer is production-grade, not an afterthought.
06
We build the data foundation that AI systems actually need — data labeling pipelines, cleaning and normalization workflows, vector database architecture (Pinecone, Weaviate, Qdrant, pgvector), knowledge base structuring, feature stores, and data governance frameworks. AI is only as good as its data — we make sure the data layer is production-grade, not an afterthought.
07
AI Integration for Enterprises
We embed AI into existing business systems where it delivers immediate ROI — CRM intelligence (Salesforce, HubSpot), ERP automation, internal copilots for support, sales, and operations teams, document processing workflows, and multi-system AI agents that orchestrate actions across your tech stack. Enterprise-grade integration with SSO, audit trails, and role-based access.
07
We embed AI into existing business systems where it delivers immediate ROI — CRM intelligence (Salesforce, HubSpot), ERP automation, internal copilots for support, sales, and operations teams, document processing workflows, and multi-system AI agents that orchestrate actions across your tech stack. Enterprise-grade integration with SSO, audit trails, and role-based access.
08
AI Security & Compliance
We secure AI systems for enterprise and regulated environments — secure inference pipelines, PII handling and data privacy controls for LLM interactions, prompt injection protection, model access control and rate limiting, and compliance frameworks for GDPR, SOC 2, HIPAA, and the EU AI Act. Security built into the AI stack, not patched on after deployment.
08
We secure AI systems for enterprise and regulated environments — secure inference pipelines, PII handling and data privacy controls for LLM interactions, prompt injection protection, model access control and rate limiting, and compliance frameworks for GDPR, SOC 2, HIPAA, and the EU AI Act. Security built into the AI stack, not patched on after deployment.
Capabilities
GPU Orchestration & Scaling
Dynamic GPU allocation across cloud providers and on-prem clusters — auto-scaling inference endpoints, spot instance management, multi-GPU training coordination, and cost-optimized compute scheduling that matches workload demand without overprovisioning.
Multi-Model Routing
Intelligent routing layers that direct queries to the right model based on complexity, cost, and latency requirements — from lightweight models for simple tasks to frontier models for complex reasoning, with automatic fallback and load balancing.
Agent & Workflow Orchestration
Framework-agnostic agent architectures — tool-use coordination, multi-step planning, memory management, human-in-the-loop checkpoints, and reliable execution of complex AI workflows with error recovery and state persistence.
LLM Evaluation & Testing
Systematic quality measurement for generative AI — automated evaluation pipelines, benchmark suites, regression testing for prompt changes, factuality scoring, and continuous monitoring of output quality across model versions.
Vector Search & Retrieval
High-performance vector database architectures for semantic search, retrieval-augmented generation, and knowledge management — with hybrid search (dense + sparse), reranking pipelines, and chunking strategies optimized for your domain.
MLOps & Model Lifecycle
End-to-end model lifecycle management — experiment tracking, model versioning, automated training pipelines, canary deployments, A/B testing, drift detection, and automated retraining triggers. Production ML, not notebook ML.
Edge & On-Prem AI Deployment
AI deployment for environments where cloud isn't an option — model quantization, on-device inference, edge computing architectures, and air-gapped deployment for sensitive or regulated environments.
AI Cost Analytics
Granular visibility into AI infrastructure spend — per-query cost tracking, model-level cost attribution, usage forecasting, budget alerting, and optimization recommendations that tie AI costs to business outcomes.
Dynamic GPU allocation across cloud providers and on-prem clusters — auto-scaling inference endpoints, spot instance management, multi-GPU training coordination, and cost-optimized compute scheduling that matches workload demand without overprovisioning.
Our Case Studies
Hola Salud
Hola Salud
A Mexican digital health platform for personalized, medically supervised weight-management plans—including GLP-1 medications.
Environmental AI
Environmental AI
Environmental compliance automation.
We built a RAG-based system that automates environmental assessment documents and regulatory templates, cutting preparation time by up to 90%.
Revvel
Revvel
An AI-powered system that turns real human performance experts into digital twins that guide women’s health and longevity.
InnerPeak.AI
InnerPeak.AI
Student mental wellness platform.
We developed a comprehensive web and mobile application that provides students and teachers with 24/7 personalized support and engaging resilience training to build essential life skills.
Hello, We Hire
Hello, We Hire
AI-driven recruitment automation platform.
We built an AI-powered recruitment system with automated pre-screening, real-time skills evaluation, and multi-stage verification that speeds up hiring by 94%.
Hola Salud
Hola Salud
A Mexican digital health platform for personalized, medically supervised weight-management plans—including GLP-1 medications.
Environmental AI
Environmental AI
Environmental compliance automation.
We built a RAG-based system that automates environmental assessment documents and regulatory templates, cutting preparation time by up to 90%.
Revvel
Revvel
An AI-powered system that turns real human performance experts into digital twins that guide women’s health and longevity.
InnerPeak.AI
InnerPeak.AI
Student mental wellness platform.
We developed a comprehensive web and mobile application that provides students and teachers with 24/7 personalized support and engaging resilience training to build essential life skills.
Hello, We Hire
Hello, We Hire
AI-driven recruitment automation platform.
We built an AI-powered recruitment system with automated pre-screening, real-time skills evaluation, and multi-stage verification that speeds up hiring by 94%.
Flexible Developer Hiring Model
Your priorities define the way we work together — from how much control you want to the level of flexibility your project demands.
Team
Augmentation
Augmentation
Quickly scale your workforce with skilled developers who integrate seamlessly into your in-house team, filling talent gaps and boosting productivity.
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Dedicated
Development Team
Development Team
A whole, cross-functional team working exclusively on your project, providing end-to-end development, management, and long-term support.
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Software Development Outsourcing
Delegate your entire project to our specialists and focus on business growth, while we handle development, delivery, and quality.
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Why Plus8Soft?
01
Experience
Multiplied by AI
Multiplied by AI
We blend deep engineering expertise with cutting-edge AI acceleration. By integrating intelligent tools into our workflow, we don't just write code—we engineer solutions faster and with higher precision.
02
Business-First
Transparency
Transparency
We look beyond the ticket. Our team operates with hyper-transparency, treating your budget and goals as our own. We align technical decisions with your business strategy to create real, measurable value.
03
Committed to
Overdelivery
Overdelivery
Meeting requirements is our baseline; exceeding them is our culture. Whether it's optimizing performance, refining UX, or anticipating future scalability, we consistently go the extra mile.
Frequently Asked Questions
What types of companies do you work with on AI platforms?
AI startups building their first production system, SaaS companies adding AI capabilities to existing products, and enterprises deploying AI across business operations. Our clients range from seed-stage GenAI startups to Fortune 500 companies embedding AI into legacy systems.
We have a working AI prototype — can you take it to production?
That's exactly what we specialize in. We take working models and build everything around them — inference infrastructure, API layers, monitoring, error handling, scaling, and the product experience. Most AI projects fail not because the model doesn't work, but because nobody engineers the production system around it.
How do you reduce AI infrastructure costs?
Multiple strategies layered together: model distillation (smaller models for simpler queries), intelligent routing (cheap models handle 60–80% of traffic), inference caching (identical or similar queries served from cache), batching (non-real-time workloads processed efficiently), and infrastructure optimization (spot instances, right-sized GPU allocation). Typical savings: 30–70% on inference costs.
Do you work with specific LLM providers or are you model-agnostic?
Model-agnostic. We work with OpenAI, Anthropic, Google, Mistral, Meta (Llama), Cohere, and open-source models. Our architectures are designed for multi-model environments — you're never locked into a single provider, and you can swap or add models as the market evolves.
How do you handle AI security and prevent prompt injection?
Multi-layered defense: input validation and sanitization, output filtering, role-based access controls, PII detection and redaction in LLM interactions, rate limiting, and monitoring for adversarial patterns. For regulated environments, we add audit logging, data residency controls, and compliance documentation for GDPR, SOC 2, HIPAA, and the EU AI Act.
How long does it take to build an AI platform?
A focused AI feature (copilot, document processing, or search): 2–4 months. A full AI platform with training pipelines, inference infrastructure, monitoring, and product UI: 6–12 months with phased delivery. We ship production increments every 2–4 weeks — you get working AI in production early, then iterate on quality, cost, and features.