AI Engineering

AI Development & Integration: Production AI, Shipped

Most AI pilots stall before production: brittle prototypes, no evaluation, nowhere to plug into your systems. Opsio's AI engineers build the real thing, custom LLM applications, RAG, agents, and ML models, then integrate them into your ERP, CRM, and data platforms with MLOps, guardrails, and a 99.9% uptime SLA behind them.

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Trusted by 100+ organisations across 6 countries

Bedrock / Azure OpenAI / Vertex

Deployment targets

MLOps

CI/CD for every model we ship

50+

Cloud & AI engineers

99.9%

Uptime SLA, 24/7 NOC

Amazon Bedrock

Azure OpenAI Service

Google Vertex AI

LangChain & LlamaIndex

Kubernetes

MLflow

Delivered by Opsio

What's Included

AI delivery is not one skill but a stack of them, and a gap anywhere breaks the whole. A brilliant model behind a flaky pipeline fails. A clean integration serving an unevaluated model ships hallucinations into production. Opsio covers the full stack with people who have built and operated these systems before, not just read about them. The six capabilities below describe what we actually deliver: generative-AI and LLM applications, retrieval-augmented generation over your data, autonomous agents that take action, custom and fine-tuned models, the MLOps and deployment machinery that keeps them healthy, and the enterprise integration that wires AI into the systems your business already runs on. Each is delivered with evaluation, guardrails, and observability built in from the start, never bolted on as an afterthought once something has already gone wrong in front of users.

Generative-AI & LLM Applications

Custom applications built on frontier and open-weight models: assistants, copilots, content and code generation, summarisation, and extraction. We handle prompts, context management, streaming, cost control, and evaluation so the product is reliable, not just impressive in a demo.

Retrieval-Augmented Generation (RAG)

Grounded answers over your documents, tickets, and knowledge bases. We design chunking, embeddings, vector search, and reranking, then tune retrieval quality so responses cite your real content and stop the model inventing facts it has no basis for.

AI Agents & Workflow Automation

Agentic systems that plan, call tools and APIs, and complete multi-step tasks across your stack. We add guardrails, human-in-the-loop checkpoints, and full action logging so autonomy stays bounded, auditable, and safe to run against live systems.

Custom ML & Model Fine-Tuning

Classical and deep models for forecasting, classification, ranking, and anomaly detection, plus fine-tuning and adapter training to specialise foundation models on your domain. We own data prep, training, evaluation, and the trade-off between fine-tuning and retrieval.

MLOps, CI/CD & Deployment

The machinery that keeps models alive: versioning with MLflow, automated evaluation gates, CI/CD pipelines, containerised serving on Kubernetes, drift and cost monitoring, and rollback. Every model we ship is reproducible, observable, and safe to update.

Enterprise Integration

Wiring AI into the systems that run your business: ERP, CRM, data warehouses, ticketing, and internal APIs. We respect existing auth, audit, and access controls, validate every output, and keep sensitive data inside your security boundary throughout.

Verified customer

Opsio's focus on security in the architecture setup is crucial for us. By blending innovation, agility, and a stable managed cloud service, they provided us with the foundation we needed to further develop our business. We are grateful for our IT partner, Opsio.

Jenny Boman

CIO · Opus Bilprovning

What is AI Development & Integration?

AI Development & Integration is how Opsio turns AI ambition into systems your business can rely on. We build custom LLM applications, retrieval-augmented generation, AI agents, and machine-learning models, then integrate them into your ERP, CRM, and data platforms with the authentication, audit, and access controls those systems demand. Around every model we wrap evaluation, guardrails, MLOps, and monitoring, so it stays accurate and healthy in production rather than degrading unwatched. We deploy on Amazon Bedrock, Azure OpenAI Service, and Google Vertex AI, choosing the platform on the merits as an AWS, Microsoft, and Google partner. Backed by 50+ engineers, an ISO 27001-certified delivery centre, a 24/7 NOC, and a 99.9% uptime SLA, Opsio ships AI that is secure, integrated, owned by you, and built to be operated, not a prototype that never reaches users.

From prototype to production-grade AI

The gap between a working AI demo and a system your business can depend on is wide, and it is where most initiatives quietly die. A notebook that answers questions on sample data is not an application. It has no authentication, no rate limits, no evaluation harness, no fallback when a model returns nonsense, and no connection to the records your teams actually work from. Opsio exists to close that gap. We are an engineering team first: we build custom generative-AI applications, retrieval systems, autonomous agents, and machine-learning models, and we ship them into the same production environments that run the rest of your business, with the operational discipline that real workloads demand. Our work spans the full lifecycle of an AI feature. On the model side, we develop and fine-tune large language models and classical ML, design retrieval-augmented generation pipelines over your proprietary content, and build agentic workflows that call tools and APIs to complete multi-step tasks. On the platform side, we stand up the data pipelines, vector stores, embedding jobs, and feature stores that feed those models. And around all of it we wrap MLOps: versioned models, automated evaluation, CI/CD, monitoring, and rollback, so the system that ships on Friday behaves the same way the following Monday and every day after.

Integration is where engineering judgement earns its keep. An LLM that cannot read a customer record or write back to your CRM is a toy. We connect AI to the systems that govern your operations, ERP, CRM, ticketing, data warehouses, and internal APIs, using the authentication, audit logging, and access controls those systems already enforce. Sensitive data stays inside your boundary. Outputs are validated before they reach a downstream system. The result is AI that participates in real workflows rather than living in a separate, disconnected sandbox that nobody trusts with production decisions.

We are deliberately cloud-pragmatic. Opsio deploys AI on Amazon Bedrock, Azure OpenAI Service, and Google Vertex AI, and we choose between them on the merits: the models you need, the regions you must stay within, the services you already run, and the commercial terms that fit your spend. We are an AWS Advanced Tier Services Partner with the AWS Migration Competency, a Microsoft Partner, and a Google Cloud Partner, so we can build natively on whichever platform serves your case best, or across more than one, without forcing a religious commitment to a single vendor's stack.

Underneath every engagement sits operational seriousness. Opsio runs a 24/7 network operations centre and backs services with a 99.9% uptime SLA, and our Bangalore delivery centre is ISO 27001-certified. That matters for AI specifically, because models drift, dependencies change, and a system left unwatched degrades. We treat an AI application like any other production service: instrumented, alerted, on-call, and improved over time. You get something that was built to be run, not a proof of concept handed over with a shrug and a slide deck claiming it was ninety percent done. Featured reading from our knowledge base: Product Engineering: Streamline Development Processes – Opsio, Continuous Integration in Software Development: A Practical Guide, and Continuous Integration for Development Process – Opsio. Related Opsio services: IoT Solution Development — Sensor to Insight, Delivered, MLOps Services — From Notebook to Production, and Enterprise RAG Chatbots — Grounded in Your Data.

Generative-AI & LLM ApplicationsAI Engineering

Retrieval-Augmented Generation (RAG)AI Engineering

AI Agents & Workflow AutomationAI Engineering

Custom ML & Model Fine-TuningAI Engineering

MLOps, CI/CD & DeploymentAI Engineering

Enterprise IntegrationAI Engineering

Amazon BedrockAI Engineering

Azure OpenAI ServiceAI Engineering

Google Vertex AIAI Engineering

Generative-AI & LLM ApplicationsAI Engineering

Retrieval-Augmented Generation (RAG)AI Engineering

AI Agents & Workflow AutomationAI Engineering

Custom ML & Model Fine-TuningAI Engineering

MLOps, CI/CD & DeploymentAI Engineering

Enterprise IntegrationAI Engineering

Amazon BedrockAI Engineering

Azure OpenAI ServiceAI Engineering

Google Vertex AIAI Engineering

How Opsio Compares

Capability	Opsio AI Engineering	Generic AI Consultancy	In-House From Scratch
Output	Production system, integrated & under SLA	Strategy deck + prototype	Depends on hiring & ramp-up
Cloud coverage	AWS, Azure & GCP certified partner	Often single-vendor or cloud-agnostic theory	Limited to in-house skills
MLOps & operations	CI/CD, monitoring, 24/7 NOC, 99.9% SLA	Rarely included	Must be built and staffed
Enterprise integration	ERP / CRM / data platforms, with auth & audit	Frequently out of scope	Possible but slow
Security posture	ISO 27001 delivery, security designed in	Variable	Depends on internal maturity
Time to production	Roughly 2-3 months for a focused use case	Often stalls at pilot	6-12+ months including hiring

Ready to get started?

Book an AI engineering consultation

What You Get

Production-grade RAG or LLM application deployed on your cloud

Custom or fine-tuned ML model with reproducible training pipeline

AI agents with guardrails and human-in-the-loop checkpoints

Data and embedding pipelines plus configured vector store

Model evaluation harness with ongoing quality scoring

MLOps pipeline: versioning, CI/CD, and safe rollback

Integration with your ERP, CRM, and internal APIs

Monitoring, alerting, and drift detection

Source code, infrastructure-as-code, and architecture documentation

Team training and operational handover, or fully managed run under SLA

Pricing & Investment Tiers

Transparent pricing. No hidden fees. Scope-based quotes.

Proof of Concept

Fixed fee, from approx. €15,000

2-4 week scoped PoC against your real data; varies by scope.

Why Choose Opsio for Cloud Services

There is no shortage of teams that can produce an AI demo. Far fewer can put one into production and keep it there. Opsio sits in that smaller group because we came to AI from a cloud-operations background, not the other way around. We have run mission-critical workloads under SLA for years, and we apply the same discipline to AI: security in the architecture from day one, evaluation before launch, monitoring after it, and a real partner attached for the life of the system. The points below are the practical reasons enterprises pick us over a generic AI shop or an internal team starting from a blank page.

Engineers, Not Slideware

We ship working code into your environment. Engagements end with a running, integrated system under SLA, not a strategy deck and a prototype that never reaches users.

Multi-Cloud By Default

AWS, Azure, and Google certified partner. We deploy on Bedrock, Azure OpenAI, or Vertex AI on the merits, and across them when that serves you best.

Security In The Architecture

ISO 27001-certified delivery. Auth, audit, data residency, and access control are designed in from the first sprint, not retrofitted after a review flags them.

Built To Be Operated

MLOps, evaluation, and a 24/7 NOC mean models are monitored, alerted, and maintained. We treat AI as a production service with a 99.9% uptime SLA.

Integration Is The Point

We connect AI to your ERP, CRM, and data platforms so it acts inside real workflows, rather than sitting in a disconnected sandbox nobody trusts.

One Partner, Full Stack

Data pipelines, models, applications, integration, and operations from a single team. No hand-offs between vendors, no gaps where accountability disappears.

Not sure yet? Start with a pilot.

Begin with a focused 2-week assessment. See real results before committing to a full engagement. If you proceed, the pilot cost is credited toward your project.

Start a Pilot

Our 4-Phase Delivery Process

We work in short, evidence-driven phases that de-risk the investment before it scales. You see something real early, decide whether to continue on the strength of measured results rather than promises, and never face a single monolithic deliverable that lands months late. Each phase has clear exit criteria, and the architecture we set in the first weeks is the one we harden and ship, not a throwaway. The four steps below take an idea from a scoped proof of concept to a deployed, integrated, and handed-over production system.

Discovery & Proof of Concept

We scope the use case, define success metrics, assess your data, and build a focused PoC against real inputs to prove value and surface risk. Timeline: 2-4 weeks.

Build

We engineer the production system: models, RAG or agents, data pipelines, evaluation harness, and guardrails, with the target cloud and architecture locked. Timeline: 4-10 weeks, scope-dependent.

Integrate & Harden

We wire AI into your ERP, CRM, and APIs, add auth, audit logging, monitoring, and load testing, and tune for cost and reliability. Timeline: 2-6 weeks.

Deploy & Handover

We deploy to production on Bedrock, Azure OpenAI, or Vertex AI, stand up MLOps and on-call, and train your team or run it for you. Timeline: 1-3 weeks, then ongoing.

Key Takeaways

Generative-AI & LLM Applications
Retrieval-Augmented Generation (RAG)
AI Agents & Workflow Automation
Custom ML & Model Fine-Tuning
MLOps, CI/CD & Deployment

Industries Served by Opsio

AI engineering is not industry-agnostic in practice. The data, the regulatory weight, and the systems AI must integrate with differ sharply between a bank and a manufacturer. Opsio has delivered cloud and AI work across regulated and data-intensive sectors, and we bring that context into the build, particularly around where data may live, what must be auditable, and which human checkpoints a workflow needs. The four sectors below are where our integration and compliance experience compounds most usefully.

Financial Services

RAG assistants over policy and product documents, fraud and anomaly models, and document automation, built with the audit trails, access controls, and data-residency constraints regulated finance demands.

Manufacturing & Industrial

Predictive maintenance, quality and defect detection, and demand forecasting, integrated with ERP and shop-floor systems so AI output reaches the people and processes that act on it.

Retail & E-commerce

Product search and recommendations, support copilots, catalogue enrichment, and demand planning, connected to commerce platforms and order systems for measurable operational impact.

SaaS & Technology

AI features embedded directly into products: in-app copilots, RAG over customer data, and agentic automation, shipped with the multi-tenant isolation and evaluation rigour software teams require.

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AI Development & Integration: Production AI, Shipped — FAQ

What is AI integration?

AI integration is the engineering work of connecting an AI model or application to the systems your business already runs on, so its outputs flow into real workflows instead of sitting in an isolated demo. In practice that means wiring a model to your CRM, ERP, data warehouse, ticketing, or internal APIs, using the authentication, audit logging, and access controls those systems already enforce. It also means validating every AI output before it reaches a downstream system, handling errors and fallbacks, and keeping sensitive data inside your security boundary. Done well, integration is what turns an interesting model into a feature your teams actually use to get work done, and it is usually where the hardest, most valuable engineering lives.

RAG or fine-tuning, which does my use case need?

They solve different problems and are often combined. Retrieval-augmented generation (RAG) gives a model access to your current, proprietary content at query time, so it answers from your documents and cites sources. It is the right default when knowledge changes often, when answers must be grounded in specific records, or when you need traceability. Fine-tuning adjusts the model's weights to learn a style, format, or specialised task, useful for consistent tone, structured output, or domain language that prompting alone cannot reliably produce. RAG handles knowledge; fine-tuning handles behaviour. Most production systems we build lean on RAG for factual grounding and reach for fine-tuning only where evaluation shows it earns its added cost and complexity. We make that call from measured results, not assumptions.

How does AI embed into our existing systems?

Through the same interfaces your other integrations use: APIs, event streams, webhooks, and database connections, governed by your existing authentication and access controls. We typically deploy the AI service inside your cloud environment, give it scoped, audited access to the data it needs, and connect it to systems like your CRM or ERP so it can read context and, where appropriate, write results back. Every AI output passes through validation and guardrails before it touches a downstream system, and every action is logged. For agentic workflows we add human-in-the-loop checkpoints where stakes are high. The goal is that AI becomes one more well-behaved service in your architecture, observable and controllable, rather than an opaque external dependency.

How do you handle security and our intellectual property?

Security is designed into the architecture from the first sprint, not reviewed at the end. Your data and prompts stay inside your security boundary; we deploy within your cloud tenancy and use enterprise model endpoints, Amazon Bedrock, Azure OpenAI, and Vertex AI, that do not train foundation models on your inputs. Access is scoped and audited, secrets are managed properly, and outputs are validated before use. Your code, models, fine-tuned weights, and data remain your intellectual property; we deliver source and documentation, with no lock-in to a proprietary Opsio runtime. Our Bangalore delivery centre is ISO 27001-certified, and we apply the same controls that underpin the mission-critical cloud workloads we already run under SLA for enterprise clients.

How long until we have something in production?

A focused proof of concept against your real data typically runs two to four weeks, enough to prove value and expose risk before larger commitment. From there, a production build is usually four to ten weeks depending on scope, followed by two to six weeks to integrate with your systems and harden for security, reliability, and cost, then one to three weeks to deploy and hand over. So a straightforward use case can reach production in roughly two to three months; a complex, multi-system integration takes longer. We work in phases with clear exit criteria precisely so you can judge progress on measured results at each gate, rather than waiting for a single large deliverable that may slip.

Which cloud should we deploy AI on?

The one that best fits your constraints, and Opsio builds natively on all three. We deploy on Amazon Bedrock, Azure OpenAI Service, and Google Vertex AI, and choose based on the specific models you need, the regions you must stay within for data residency, the cloud you already operate, and the commercial terms that suit your spend. If you are already heavily on AWS, Bedrock usually wins on integration and governance; deep Microsoft estates often favour Azure OpenAI; Vertex AI is compelling for certain Google models and data-platform pairings. As an AWS Advanced Tier, Microsoft, and Google Cloud partner, we have no incentive to push one over another, and we will tell you plainly which we recommend and why.

Can you embed AI into our existing product rather than build something separate?

Yes, and that is often the highest-value path. Rather than standing up a parallel tool, we add AI features directly inside the product your customers or staff already use: an in-app copilot, RAG over their own data, search that actually understands intent, or agentic automation of a manual flow. We work within your codebase, deployment pipeline, and design system, and we build for the multi-tenant isolation and per-customer data boundaries a real product requires. Evaluation and guardrails ship with the feature so quality is measured, not hoped for. The outcome is AI that feels like a native part of your product, maintained alongside your existing release process, rather than a bolt-on that complicates your roadmap.

What do we actually receive at the end of an engagement?

A running, integrated production system, not a report. Concretely, that means the deployed application or model on your chosen cloud, the source code and infrastructure-as-code, the data and retrieval pipelines, an evaluation harness that scores quality on an ongoing basis, the MLOps pipeline for versioning and safe updates, integration with your named systems, monitoring and alerting, and documentation plus handover so your team can operate or extend it. Where you prefer, we continue to run and improve the system for you under SLA through our 24/7 NOC. Everything we build is yours, including models and fine-tuned weights, with no dependency on a closed Opsio runtime that would trap you later.

How much does it cost to build an AI solution?

Cost depends on scope, integrations, data readiness and security needs. Opsio usually starts with a fixed-fee proof of concept to validate value quickly. From there a full build runs as a fixed-scope project or a dedicated AI team billed monthly, depending on how much you want to ship and evolve. The main drivers are the number of integrations, data quality, compliance requirements and ongoing model operations. Exact pricing is provided via a quote once we have scoped your solution together.

Still have questions? Our team is ready to help.

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Editorial standards: Written by certified cloud practitioners. Peer-reviewed by our engineering team. Updated quarterly.