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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.

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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

AI Infrastructure & Platform Engineering

We build the foundational systems that make AI work at scale, model training pipelines, distributed inference architectures, GPU orchestration across AWS, GCP, Azure, and on-prem clusters.

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.

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.

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.

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.

Data Engineering for AI

We build the data foundation that AI systems actually need, data labeling pipelines, cleaning and normalization workflows, vector database architecture, knowledge base structuring, feature stores, and data governance frameworks.

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.

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.

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.

Our Case Studies

Hola Salud

Digital healthcare platform for Mexico.

We built a complete healthcare ecosystem, including a patient app, a doctor’s portal, and an admin backend, with self-registration, appointment booking, medical records, and integrated Stripe and Oxxo payments.

Read the entire case

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%.

Read the entire case

Revvel

AI-powered women’s health platform.

We developed an AI platform that turns elite health experts into digital twins and reviews years of patient data in under 60 seconds, delivering risk assessments up to 10x faster and cutting time-to-care from days to minutes.

Read the entire case

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.

Read the entire case

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%.

Read the entire case

Hola Salud

Digital healthcare platform for Mexico.

Read the entire case

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%.

Read the entire case

Revvel

AI-powered women’s health platform.

Read the entire case

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.

Read the entire case

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%.

Read the entire case

View all case studies

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

Quickly scale your workforce with skilled developers who integrate seamlessly into your in-house team, filling talent gaps and boosting productivity.

Dedicated Development Team

A whole, cross-functional team working exclusively on your project, providing end-to-end development, management, and long-term support.

Software Development Outsourcing

Delegate your entire project to our specialists and focus on business growth, while we handle development, delivery, and quality.

Why Plus8Soft?

Experience
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.

Business-First
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.

Committed to
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.

AI Platform Development

AI Engineering That Ships — Not Just Experiments

Services

Capabilities

Our Case Studies

Flexible Developer Hiring Model

Why Plus8Soft?

Frequently Asked Questions

Turn AI Into a Product, Not a Project