We run your complete AI portfolio and build/maintain custom ML models across your full data pipeline. Our team keeps your AI ecosystem healthy by handling data ingestion, feature engineering, model training, containerized deployment, 24/7 performance monitoring, and security.
Fully managed infrastructure24/7 engineering supportISO 27001 certifiedSOC 2 Type II audited
100+
Managed AI Engagements Delivered
95%
On-Time Project Delivery
40%
Average Reduction in Time-to-Insight
30+
Security Audits Passed Annually
Managed AI services are the ongoing operational and strategic support layer that sits between your business and your AI systems. It covers the infrastructure your models run on, the governance frameworks that keep them compliant, the pipelines that feed them clean data, and the human oversight that catches what automation misses.
Unlike a one-time AI implementation project, a managed AI service runs continuously. The provider monitors model performance, handles retraining cycles, manages prompt engineering and cost optimization for LLMs, and evolves your AI stack as your business requirements change.
For companies operating in regulated industries or complex enterprise environments, managed AI services also includes compliance monitoring, bias auditing, and vendor-neutral architecture guidance: the work that typically falls through the gap between a systems integrator and an internal AI team.
Here are our seven operational pillars covering the full lifecycle of enterprise AI, from infrastructure through governance to strategic direction.
Our AI development company handle the infrastructure layer your models depend on across the full deployment lifecycle. Our MLOps stack spans MLflow, Kubeflow, Seldon, and TensorFlow Serving, deployed on whichever cloud environment fits your requirements.
As a leading Gen AI development company, we manage the operational layer across GPT, Claude, Gemini, Llama, Qwen, and Kimi. Our AI Model Router implementation prevents budget overruns by intelligently routing requests to the most cost-efficient model without compromising output quality. For organizations with significant monthly LLM API spend, this layer alone consistently reduces inference costs by 25 to 40 percent.
Digital is Simple holds ISO 27001, SOC 2, and ISO 9001:2015 certifications. We implement security guardrails, conduct bias auditing across model outputs, and map your AI systems to relevant regulatory frameworks including GDPR, the EU AI Act, HIPAA, and sector-specific requirements. Every governance engagement produces documentation your legal and compliance teams can actually use.
We connect AI capabilities into your existing application stack using MCP, LangChain, LangGraph, CrewAI, and AutoGen. Whether your environment runs SAP, Salesforce, SharePoint, or a proprietary CRM, we build integrations that are scalable, secure, and maintainable.
Models are only as reliable as the data they run on. Our Managed services company audit and clean data pipelines built on Apache Spark, Kafka, Pandas, and Airflow, and build the vector database infrastructure required for semantic search and RAG. We ensure your data architecture can support the AI workloads you are planning for the next 18 months.
Before operational work begins, we run an AI readiness assessment covering your infrastructure, data maturity, regulatory exposure, and internal capability gaps. We identify where your teams lose time, money, and visibility, not just where you want AI.
AI systems introduce attack surfaces traditional security frameworks were not built to handle. We manage the full security layer of your AI environment on a continuous basis, from guardrail implementation to adversarial testing.
Our AI development company handle the infrastructure layer your models depend on across the full deployment lifecycle. Our MLOps stack spans MLflow, Kubeflow, Seldon, and TensorFlow Serving, deployed on whichever cloud environment fits your requirements.
As a leading Gen AI development company, we manage the operational layer across GPT, Claude, Gemini, Llama, Qwen, and Kimi. Our AI Model Router implementation prevents budget overruns by intelligently routing requests to the most cost-efficient model without compromising output quality. For organizations with significant monthly LLM API spend, this layer alone consistently reduces inference costs by 25 to 40 percent.
Digital is Simple holds ISO 27001, SOC 2, and ISO 9001:2015 certifications. We implement security guardrails, conduct bias auditing across model outputs, and map your AI systems to relevant regulatory frameworks including GDPR, the EU AI Act, HIPAA, and sector-specific requirements. Every governance engagement produces documentation your legal and compliance teams can actually use.
We connect AI capabilities into your existing application stack using MCP, LangChain, LangGraph, CrewAI, and AutoGen. Whether your environment runs SAP, Salesforce, SharePoint, or a proprietary CRM, we build integrations that are scalable, secure, and maintainable.
Models are only as reliable as the data they run on. Our Managed services company audit and clean data pipelines built on Apache Spark, Kafka, Pandas, and Airflow, and build the vector database infrastructure required for semantic search and RAG. We ensure your data architecture can support the AI workloads you are planning for the next 18 months.
Before operational work begins, we run an AI readiness assessment covering your infrastructure, data maturity, regulatory exposure, and internal capability gaps. We identify where your teams lose time, money, and visibility, not just where you want AI.
AI systems introduce attack surfaces traditional security frameworks were not built to handle. We manage the full security layer of your AI environment on a continuous basis, from guardrail implementation to adversarial testing.
Not Sure If You Need Managed AI Services?
We assess your current AI environment, identify operational gaps, and tell you honestly what needs managing and what does not.
As a leading managed AI services company, we work across every major framework and model. Each tool is selected to solve real business problems, optimize costs, and deliver measurable results.
OpenClaw
SuperAGI
LangChain
LangGraph
AutoGPT
CrewAI
Microsoft AutoGen
SemanticKernelMany companies have handed us their AI environments. Here is what we did with three of them
A SaaS company came to us after deploying a basic AI support tool that was producing inconsistent responses. The model was live but unmanaged.
We took over the full operational layer. We rebuilt the escalation architecture so complex cases reached human agents with a structured summary already attached, implemented a token cost routing system, and introduced monthly model performance reviews tied to customer satisfaction data
Twelve months in, response accuracy has improved consistently quarter over quarter, manual triaging has been eliminated for the majority of ticket categories, and the client has full monthly visibility into model performance, cost trends, and compliance status.
A professional services firm had deployed an internal knowledge assistant built on their own documentation. Six months in, the system was surfacing outdated content and had no access control enforcement.
Our team took on the managed operations engagement with a full retrieval audit, identifying which document sources were contaminating the index with outdated or conflicting content. We implemented a governed ingestion pipeline, access-tier enforcement, and scheduled re-indexing.
Retrieval accuracy improved significantly within the first quarter. Onboarding time for new hires reduced measurably as the system became reliable enough to use from day one.
A mid-sized manufacturer had integrated an AI layer into their ERP environment but had no one monitoring it. Model outputs were informing procurement and maintenance decisions with no performance validation.
Our AI developers ran an environment audit covering data flow integrity, model output accuracy against ground truth, and the reliability of the ERP data streams feeding the system.
The manufacturer now operates with real-time visibility into both AI system health and underlying data quality. The operations team has a named account engineer, monthly performance reports, and a governance framework that satisfies their ISO audit requirements.
There are many reasons for that. Here are some of them:
We are partners with AWS, Azure, Google Cloud, and Oracle, but not incentivized to keep you on any single platform. We work across GPT, Claude, Gemini, Llama, DeepSeek, Qwen, and Kimi. Your architecture fits your requirements, not ours.
Usage-based AI costs are one of the primary reasons enterprise AI budgets collapse within six months. Every Digital is Simple engagement is priced on a fixed monthly retainer, scoped in advance, with cost overruns covered on our side.
Over 10 years and 100+ AI solutions delivered, we have connected modern systems to SAP environments, proprietary CRMs, and on-premise data stacks. If another vendor has told you your stack is too complex, that is usually a scoping problem.
Your data does not move unless you authorize it. We build on-premise AI solutions for government, defense, finance, insurance, and other regulated sectors, connecting open-source models including DeepSeek, Llama, and Kimi in zero-connectivity environments where required. ISO 27001, SOC 2, and ISO 9001:2015 certified.
Across 100+ AI solutions delivered, we have built reusable governance frameworks, integration patterns, and compliance mappings for 9 industries. Your deployment starts 6 to 8 weeks ahead of a generic scoped engagement because the domain-specific groundwork is already done.
Every autonomous AI workflow we manage includes defined escalation points where human review is mandatory before the system acts. This is how you deploy AI in regulated, client-facing, or high-stakes operational environments without catastrophic tail risk.
As AI moves from experimentation to production, many organizations discover that building models is only a small part of the challenge. This is where our managed AI services create measurable value.
| Area | Internal Team | Managed AI Service |
|---|---|---|
| Monitoring | Business-hours support | 24/7 monitoring |
| Model Drift | Reactive fixes | Continuous detection & retraining |
| Compliance | Additional workload | Built-in governance |
| Security | Shared responsibility | Dedicated AI security controls |
| Cost Optimization | Manual reviews | Continuous optimization |
| Infrastructure | Managed in-house | Fully managed |
| Reporting | Fragmented visibility | Centralized dashboards |
| Expertise | Limited to team size | Access to AI specialists |
| Scalability | Requires hiring | Scales on demand |
| Focus | Maintenance-heavy | Focus on innovation |
We help you pick the right engagement based on your current AI maturity.
As a leading Managed AI service provider, we select the tools that fit your environment and your requirements.
LangChain
LangGraph
Microsoft AutoGen
CrewAI
OpenClaw
SuperAGI
FastAPI / Flask backends
Async task managers (Celery, Ray)
LLaMA (Meta)
DeepSeek
Mistral
Mixtral
Falcon
Phi (Microsoft)
Gemma
Pinecone
Weaviate
Milvus
Chroma
Qdrant
OpenAI embeddings
BGE
Cohere embeddings
TensorFlow
PyTorch
Scikit-learn
XGBoost
LightGBM
CatBoost
spaCy
NLTK
Hugging Face Transformers
SentenceTransformers
Haystack
OpenCV
YOLO
Detectron2
TensorFlow Vision
PyTorch Vision
MMDetection
Apache Spark
Dask
Pandas
Apache Kafka
Apache Flink
AWS Kinesis
Google Pub/Sub
PostgreSQL
MySQL
Snowflake
MongoDB
Cassandra
Redis
AWS
Google Cloud Platform (GCP)
Microsoft Azure
Google Vertex AI
Azure AI Studio
AWS Bedrock
Docker
Kubernetes
Helm
Istio
Argo Workflows
MLflow
Kubeflow
Weights & Biases
SageMaker
Vertex AI Pipelines
Azure ML
Model versioning
Drift detection
Monitoring
CI/CD for ML
LangSmith
Helicone
PromptLayer
Arize AI
Datadog
Grafana
ELK Stack
n8n
Zapier (enterprise)
Make
Airflow
Temporal
Prefect
LangChain
LangGraph
Microsoft AutoGen
CrewAI
OpenClaw
SuperAGI
FastAPI / Flask backends
Async task managers (Celery, Ray)
LLaMA (Meta)
DeepSeek
Mistral
Mixtral
Falcon
Phi (Microsoft)
Gemma
Pinecone
Weaviate
Milvus
Chroma
Qdrant
OpenAI embeddings
BGE
Cohere embeddings
TensorFlow
PyTorch
Scikit-learn
XGBoost
LightGBM
CatBoost
spaCy
NLTK
Hugging Face Transformers
SentenceTransformers
Haystack
OpenCV
YOLO
Detectron2
TensorFlow Vision
PyTorch Vision
MMDetection
Apache Spark
Dask
Pandas
Apache Kafka
Apache Flink
AWS Kinesis
Google Pub/Sub
PostgreSQL
MySQL
Snowflake
MongoDB
Cassandra
Redis
AWS
Google Cloud Platform (GCP)
Microsoft Azure
Google Vertex AI
Azure AI Studio
AWS Bedrock
Docker
Kubernetes
Helm
Istio
Argo Workflows
MLflow
Kubeflow
Weights & Biases
SageMaker
Vertex AI Pipelines
Azure ML
Model versioning
Drift detection
Monitoring
CI/CD for ML
LangSmith
Helicone
PromptLayer
Arize AI
Datadog
Grafana
ELK Stack
n8n
Zapier (enterprise)
Make
Airflow
Temporal
Prefect
Every engagement at our AI consulting company follows a structured four-stage process.
We assess your existing AI deployments, data infrastructure, compliance obligations, and integration landscape. You receive a written audit report identifying gaps, risks, and prioritized opportunities, regardless of whether you proceed to an operational engagement.
Our AI experts design the operational architecture for your managed AI environment: vendor selection, data flow mapping, governance framework alignment, and human-in-the-loop checkpoints. We present this as a documented blueprint before any implementation begins.
We deploy in phases, starting with the highest-priority workloads. Each phase includes defined success criteria, rollback protocols, and your sign-off before advancing.
From go-live, your environment is monitored continuously. Monthly reporting covers model performance, cost trends, compliance status, and planned optimizations. You have a named account engineer throughout.
AI operations maturity varies. Your engagement model should reflect where you are today and scale as your managed AI environment grows.
Full managed AI audit before work begins Governance and compliance docs in scope Drift detection configured at deployment Human-in-the-loop checkpoints defined upfront Fixed retainer, no compute cost pass-through Ideal for unmanaged or failed AI rollouts
Named MLOps engineer on your deployments Continuous model monitoring and retraining Monthly compliance and governance reporting LLM cost optimization and token routing managed Data pipeline and vector database maintenance included Ideal for regulated environments running multiple models
Single model deployment, full monitoring from day one Governance documentation from month one Retraining schedule managed as data matures LLM cost controls before spend scales Scope expands as new models go live Ideal for pilots moving into production
We have active deployments across nine industries. Here is where we operate.
We review your current environment, identify your highest-risk gaps, and produce a written report with a prioritized action plan.
A consulting engagement delivers a project: a model, an integration, a strategy document. It ends. A managed AI service is an ongoing operational commitment. We monitor your models after they deploy, handle retraining, manage costs, and maintain compliance documentation on a continuous basis. For companies where AI is becoming infrastructure rather than a project, that distinction matters.
Digital is Simple offers fixed-scope, fixed-cost engagements starting from $2,500 for straightforward API integrations, scaling to $50,000 to $150,000+ for production-grade custom AI systems depending on complexity. Dedicated team engagements for ongoing managed operations are structured as monthly retainers with scope and costs agreed upfront. We do not pass through cloud compute costs at margin, and we do not bill by the hour for model retraining cycles.
Because running models in production is different from building them. Your data science team's highest-value work is model development and strategic AI direction. MLOps infrastructure, compliance monitoring, LLM cost optimization, and vendor management are not the best use of that capability. We handle the operational layer so your internal team focuses on the work they were hired to do.
Data sovereignty is designed into the architecture before a single integration is built. Digital is Simple holds ISO 27001, SOC 2, and ISO 9001:2015 certifications. We build on-premise AI environments using open-source models for clients where zero external data movement is required. We document all data flows, maintain residency compliance for each jurisdiction, and produce audit-ready evidence for your legal and compliance teams under GDPR, HIPAA, the EU AI Act, and sector-specific regulations.
Yes, and we do this regularly. The first step is an environment audit covering architecture, data flows, model documentation, and vendor dependencies. We then produce a transition plan identifying what can be migrated, what should be rebuilt, and what timelines are realistic. Vendor lock-in is a solvable problem in most cases, and we will tell you honestly when it is not.
Hyperscaler platforms are infrastructure. They give you compute, APIs, and tooling, but not governance, compliance documentation, human oversight protocols, legacy integration, or strategic direction. We sit on top of whichever infrastructure is right for your environment and provide the operational and governance layer the platforms do not. We are also not incentivized to keep you on any single cloud.