enterprise AI
development services
that power your
business systems

Full-stack AI engineered into your enterprise infrastructure, internal applications and SaaS workflows with Algoryte's enterprise AI services.

See what enterprise AI looks like on your systems.

schedule an AI scoping call

Algoryte's enterprise AI development services add AI to the systems your business already runs, in three places: on your cloud AI platform (Microsoft, AWS, Google, IBM, Huawei or Alibaba), inside the applications you own, and across the SaaS platforms your teams use, such as CRM, ERP and HRMS. Everything is deployed in your own cloud, under your identity and access controls, and measured against a baseline.

what is enterprise AI?

Enterprise AI is the use of machine learning, language models, computer vision and AI agents inside an organization's own systems and workflows, deployed under the security, governance and scale that a large business requires. It differs from consumer AI tools in where it runs (your cloud and applications), what it can see (your data, within each user's permissions) and how it is controlled (audit logs, approvals and monitoring).

what are enterprise AI
development services?

Enterprise AI development services design, build, integrate and run AI inside a company's existing infrastructure, applications and SaaS platforms. Business software should learn from how people use it, remove manual effort and return a measurable result. Adding AI does not mean starting from scratch: we integrate it into your current stack, deploy it in your own cloud under your security and access controls, and surface it inside the software your teams already use.

Where you want a decision before a build, start with enterprise AI consulting.

enterprise AI development services at a glance

what it is	AI built into your cloud platform, your own applications and your SaaS stack
cloud AI platforms	Microsoft Foundry and Azure, AWS Bedrock and SageMaker, Google Gemini Enterprise Agent Platform, IBM watsonx, Huawei Cloud ModelArts, Alibaba Cloud Model Studio
custom applications	AI-enablement of existing apps, AI-first workflow redesign, or AI-native builds
capabilities	Machine learning, natural language processing, computer vision, AI automation, generative AI, RAG, fine-tuned models and in-app agents
across SaaS	Cross-system agents, MCP connections, data reconciliation, decision models, governance and escalation
where it runs	Your cloud account or project, under your identity provider, network boundaries and keys
engagement models	Dedicated team, project-based delivery, fractional support or sprint-based work

three ways we bring AI
into your enterprise

Most enterprise AI programs use more than one of these routes. Which one leads depends on where your data lives and where your people work.

the three routes for enterprise AI and when each one fits
route	what we build	when it fits
On your cloud AI platform	Models, agents, retrieval and governance using the services your cloud provider already offers	You have standardized on Microsoft, AWS, Google, IBM, Huawei or Alibaba Cloud
Inside your custom applications	AI features, AI-first workflows and AI-native products in the software you own	The AI should be part of your product or internal platform
Across your SaaS platforms	Agents, MCP connectors and reconciliation that work across CRM, ERP, SCM and HRMS	Your processes span several vendors' systems

enterprise AI on
your cloud platform

We build enterprise AI with the models, agents, retrieval and governance services each platform provides, deployed in your account and under your controls. This table compares the main building blocks; the sections below list what we build on each platform.

enterprise AI platforms compared by building block
platform	models	agents	retrieval and data	governance
Microsoft	Foundry Models	Foundry Agent Service	Azure AI Search, Fabric, Azure Databricks	Foundry Control Plane, Entra ID
AWS	Amazon Bedrock	Bedrock AgentCore	Bedrock Knowledge Bases, S3 Vectors, OpenSearch	Bedrock Guardrails, IAM, VPC
Google Cloud	Gemini Enterprise Agent Platform (Model Garden)	Gemini Enterprise, ADK and Agent Engine	BigQuery AI, AlloyDB and Spanner vector search	IAM, VPC controls
IBM	watsonx.ai with Granite	watsonx Orchestrate	watsonx.data	watsonx.governance
Huawei Cloud	Pangu models, MaaS	ModelArts Studio	OBS, ModelArts data services	Huawei Cloud IAM
Alibaba Cloud	Model Studio (Qwen)	Wuying AgentBay	DashVector, OpenSearch	RAM access control

what we build
on each platform

microsoft

Microsoft Foundry for models, agents, knowledge and governance; Azure Machine Learning for custom models; Fabric and Databricks for the data underneath; all under your Entra identity and access controls.

enterprise AI on microsoft
service	what it is	what we build
Foundry Models	Models from OpenAI, Anthropic, Meta, Mistral, Cohere and Microsoft Phi on one platform	Model selection per task, managed inside one security boundary
Foundry Agent Service	Managed infrastructure where AI agents run	Agents designed, built and deployed on it, with the runtime set to your tenant, identity and network rules
Azure AI Search	Managed retrieval over enterprise content	Grounding that gives employees cited answers from policies, contracts and manuals
Foundry Tools	Prebuilt APIs for document extraction, speech, image analysis and translation	Integration into your workflows
Foundry Control Plane	Monitoring and governance for every AI system	Agent health, cost, tracing, evaluations and audit logs in one place
Azure Machine Learning	Training and deployment of custom models	Demand forecasting, risk scoring and defect detection on your data
Microsoft Fabric	Business data unified in OneLake	Pipelines that connect current data to Foundry
Azure Databricks	Large-scale data processing	Cleaning and modeling transaction histories, logs and sensor streams for AI

AWS

Bedrock for models and agents, SageMaker for custom models and the data services underneath, all inside your AWS account under your IAM and VPC boundaries. We also use PyTorch, TensorFlow and Hugging Face on SageMaker, EC2 GPU instances and KMS where the work requires them.

enterprise AI on AWS
service	what it is	what we build
Amazon Bedrock	Models from Anthropic, Meta, Mistral, Amazon Nova and OpenAI through one API	Model selection per task under your account's controls, with your data excluded from model training
Bedrock AgentCore	AWS's managed runtime for production agents	Agents with policy controls, memory and VPC-only deployment where isolation is required
Bedrock Knowledge Bases	Managed retrieval over your documents	Grounded answers from your files without custom RAG infrastructure
Bedrock Guardrails	Safety controls on model and agent outputs	Content filters, topic limits and PII handling tuned to your policies
AWS AI services	Textract, Transcribe, Polly, Rekognition and Translate	Document, speech, image and translation steps in your workflows
Amazon SageMaker	Training and deployment of custom models	Forecasting, scoring and classification models, retrained as data changes
S3 Vectors and OpenSearch	Vector storage and search	The embedding and search layer your AI systems query
AWS Glue and Redshift	Data preparation and warehousing	Pipelines that feed clean, current data to the systems above

google cloud

The Gemini Enterprise Agent Platform (formerly Vertex AI) for models and agents, BigQuery for AI on your data, and prebuilt APIs for applied tasks, all in your Google Cloud project under your IAM. We serve on GKE and Cloud Run and train on GPU and TPU compute where needed.

enterprise AI on google cloud
service	what it is	what we build
Gemini Enterprise Agent Platform	Google's AI platform, with Gemini, Claude, Gemma and open models in Model Garden	Model selection per task, plus custom ML trained, tuned, deployed and monitored
Gemini Enterprise	Google's platform for running AI agents across an organization	Agents grounded in your data through its connectors, with security and access set up
Agent Development Kit and Agent Engine	Google's agent framework and managed runtime	Custom agents built in ADK and deployed to Agent Engine in your project
BigQuery AI	ML and generative AI in the data warehouse through SQL	Analytics and AI workflows that keep data where it is
Vector search	Embeddings and search in BigQuery, AlloyDB and Spanner	Retrieval that grounds AI in your operational data
Document AI	Extraction, classification and splitting of documents	Intake and back-office document workflows
Speech, Vision and Natural Language APIs	Prebuilt APIs for speech, image, video, translation and text	Applied AI steps in your workflows
CX Agent Studio	Google's platform for customer-facing voice, chat and messaging agents	Conversation flows connected to your systems

IBM

IBM's watsonx portfolio for models, agents, data and governance, deployed in your environment under your controls, with Granite models for open-weight deployment where they fit.

enterprise AI on IBM watsonx
service	what it is	what we build
watsonx.ai	IBM's AI studio, with Granite and hosted open-source models	Agents, model customization and fine-tuning, RAG over your knowledge, and classic ML models
watsonx Orchestrate	IBM's platform for AI assistants and agents in business processes	Agents connected to your systems and managed in production
watsonx.data	IBM's data foundation across hybrid environments	The governed data layer that feeds current data to AI
watsonx.governance	Risk, compliance and explainability for AI	Monitoring and audit for every model and agent from the start

huawei cloud

Huawei Cloud's AI stack for development, foundation models, model access and applied services, deployed in your Huawei Cloud environment on OBS storage and Ascend compute, under Huawei Cloud IAM.

enterprise AI on huawei cloud
service	what it is	what we build
ModelArts	Huawei Cloud's AI development platform for the full ML lifecycle	Custom models trained on your data
Pangu models	Huawei's foundation and industry models for language, vision, multimodal, prediction and scientific computing	Model, application and agent development in ModelArts Studio
MaaS	Token-based API access to Pangu and open models such as DeepSeek, Qwen and GLM	Applications on these models where they fit the task
AI services	Prebuilt OCR, image recognition, content moderation and video analysis	Applied AI steps in your workflows

alibaba cloud

Model Studio for generative AI, PAI for custom models, and the retrieval and applied services around them, deployed in your Alibaba Cloud environment on OSS storage, under RAM access control.

enterprise AI on alibaba cloud
service	what it is	what we build
Model Studio	Alibaba's generative AI platform built around the Qwen model family	Applications on Model Studio, or Qwen deployed directly in your environment on any cloud
Platform for AI (PAI)	End-to-end ML platform for data, training, tuning and deployment	Custom models trained on your data
Wuying AgentBay	Cloud execution environments for AI agents, with MCP-integrated tools	Agents deployed and run within your permissions
DashVector	Managed vector retrieval	The embedding and retrieval layer your applications and agents query
Prebuilt AI services	Chatbots, speech, machine translation, image search and visual intelligence	Applied AI steps in your workflows
OpenSearch and AIRec	Managed intelligent search and recommendations	Search and recommendations tuned to your catalog and users

enterprise AI in your
custom applications

The applications you own, such as your product, internal platforms and customer portals, are where AI creates the most differentiated value, because what gets built is yours alone. We meet each system where it is:

AI-enablement, AI-first and AI-native compared
approach	what it means	when it fits
AI-enablement	Adding AI features to an existing application	The workflow is sound and AI removes manual steps
AI-first	Redesigning a workflow around AI	The process itself should change, not only speed up
AI-native	Building with AI at the core from day one	The system is new

We choose the intelligence for the problem, never the other way around.

AI capabilities we build
into your applications

AI capabilities and the features we build with them
capability	features we build
Machine learning	Recommendations that surface the right product or content; predictive scores that rank leads, flag risk and surface churn; forecasts for demand, revenue and inventory; anomaly detection that catches fraud or errors early
Natural language processing	Chatbots that answer from your knowledge base; voice assistants; document understanding that pulls fields from contracts and forms; sentiment analysis of reviews and tickets; FAQ automation
Computer vision	OCR that turns receipts, IDs and forms into records; image recognition that catches defects, verifies uploads and flags anomalies; visual search from a photo
AI automation	Tickets, requests and documents sorted and routed; approvals and validations without a queue; reports generated and delivered on schedule
Generative AI and LLMs	Product descriptions, emails and reports drafted in your voice; conversational interfaces; summaries of long documents, threads and calls
Full-stack additions	RAG that grounds answers in your data; fine-tuned models where off-the-shelf models fall short; in-app AI agents that run multi-step tasks in your system

enterprise AI across
your SaaS platforms

CRMs, ERPs, SCM and HRMS platforms include built-in AI, but it only works inside each vendor's boundaries. Processing an order, managing a claim or onboarding an employee spans several systems. We build the integration layer that connects them:

cross-system
AI agents

Agents that carry a process across your stack, from CRM to ERP, email and finance, completing multi-step tasks and updating records without manual data transfer. See agentic AI.

MCP
connections

Connectors built on the Model Context Protocol that let any LLM or internal tool read from and write to your systems securely, without a custom integration for every pair of tools.

data reconciliation
engines

Agents that scan, match and resolve duplicate records and outdated fields across systems, so your core platforms share one source of truth.

proprietary
decision models

Custom ML and fine-tuned models trained on your business rules, such as credit limits, compliance frameworks and pricing logic, to make context-aware decisions.

governance
and escalation

Role-based access, complete audit logs and human approval for high-stakes decisions before any system update is final.

our enterprise AI
development process

Every enterprise AI build follows the same six steps, whichever route it takes.

why choose algoryte
for enterprise AI

six cloud
platforms,
one team

We build on Microsoft, AWS, Google, IBM, Huawei and Alibaba Cloud, so the recommendation follows your stack and region, not our partnerships.

your cloud,
your
controls

Everything runs in your account or project, under your identity provider, network rules and encryption keys. Your data stays inside your perimeter.

platform services
first, custom code
where it pays

We use managed platform services where they do the job and write custom code only where your workflow needs it, which keeps cost and maintenance down.

governance
built in from
the first release

Role-based access, audit logs, evaluations and human approval are part of the first release.

measured
against a
baseline

We record the baseline before the build and report results in the metrics your business already uses.

enterprise AI
by industry

enterprise AI for banking & financial services

Credit and fraud scoring
KYC document checks
Relationship manager copilots

Systems it connects: Core banking, CRM, document stores

enterprise AI for insurance

Claims intake and triage
Underwriting assistants
Policy Q&A

Systems it connects: Policy admin, claims, document systems

enterprise AI for healthcare

Prior authorization support
Clinical document summaries
Scheduling assistants

Systems it connects: EHR, payer portals, scheduling

enterprise AI for retail & e-commerce

Demand forecasting
Product content generation
Customer service agents

Systems it connects: ERP, PIM, order management, helpdesk

enterprise AI for manufacturing

Predictive maintenance
Visual quality inspection
Supplier document processing

Systems it connects: MES, ERP, maintenance systems

enterprise AI for logistics & supply chain

ETA prediction
Document extraction
Exception handling agents

Systems it connects: TMS, WMS, carrier APIs

enterprise AI for professional services

Proposal drafting
Knowledge search
Time and billing automation

Systems it connects: PSA, CRM, document management

enterprise AI for SaaS & software

In-product copilots
Support deflection
Churn prediction

Systems it connects: Your product, CRM, billing, helpdesk

enterprise AI for public sector & education

Case summarization
Citizen and student assistants
Document intake

Systems it connects: Case management, LMS, records systems

engagement models
for enterprise AI

dedicated
enterprise AI
team

Full-time engineers and specialists inside your team, from architecture through operations.

Best for: A program across several systems and business units

project-based
delivery

A defined scope, timeline and deliverables, from scoping to a system in production.

Best for: One use case on one route

fractional
AI support

Part-time specialists for monitoring, retraining and tuning live systems.

Best for: Systems already in production

sprint-based
engagements

Short cycles on one outcome: a proof of concept, an integration or a performance fix.

Best for: A bounded goal with a clear finish line

ready to connect
your enterprise
systems with AI?

talk to our AI architect

tech stack & tools

cloud AI platforms

microsoft foundry

amazon bedrock

gemini enterprise agent platform

IBM watsonx

huawei cloud modelArts

alibaba cloud model studio

models

openAI GPT

anthropic claude

google gemini

llama

mistral

qwen

IBM granite

ML frameworks

pyTorch

tensorFlow

scikit-learn

XGBoost

hugging face

agents and integration

langGraph

model context protocol (MCP)

REST APIs

webhooks

data

microsoft fabric

databricks

bigQuery

amazon redshift

snowflake

retrieval

azure AI search

bedrock knowledge bases

pgvector

pinecone

security and identity

microsoft entra ID

AWS IAM

google cloud IAM

KMS

VPC controls

observability

foundry control plane

amazon cloudWatch

openTelemetry

langSmith

FAQs

1. What is enterprise AI and what does it include?

Enterprise AI is AI deployed across an organization's own software, data and workflows to automate processes and support decisions. It includes custom decision models, AI agents that work across systems, fine-tuned language models, retrieval over company knowledge, and the data pipelines, security and monitoring that keep these systems reliable inside core platforms such as CRMs and ERPs.

2. What are the security implications of integrating AI across enterprise data?

Connecting AI to internal records creates risks of unauthorized access, compliance breaches and data leaving your perimeter. These risks are managed by running AI inside your own cloud, enforcing role-based access at retrieval and at every tool call, keeping audit logs, excluding your data from model training, and making agents act only within your identity and network policies.

3. How do you add AI to legacy systems?

Adding AI to legacy systems does not require a rewrite. We connect to the system through its existing APIs, database or an MCP server, and add the AI as a service beside it: a model, an agent or a retrieval layer. The legacy system gains predictions, automation and natural-language access while its core logic and day-to-day operations stay as they are.

4. How do you overcome data silos in AI integration projects?

Data silos are overcome with a shared access layer, which avoids a large migration. We connect SaaS tools, databases and ERPs through MCP connectors and APIs, use reconciliation engines to keep records consistent across systems, and build retrieval over the combined data, so AI works from one current view while each system keeps its own data and permissions.

5. What KPIs measure enterprise AI integration success?

Enterprise AI success is measured on operational results against a baseline recorded before the build: cycle time per task, manual data entry error rate, cost per transaction, decision accuracy against your business rules, and the share of cases completed without manual intervention. Adoption by the people who use the system is tracked alongside, since unused AI returns nothing.

6. How do you choose an enterprise AI development company?

Choose an enterprise AI development company on checkable evidence: experience with your cloud platform, the ability to work inside your perimeter and identity controls, a clear view of which steps need AI and which do not, governance in the first release, and results measured against a baseline. Ask who owns the code and how the system is run after launch.

7. Which cloud AI platform should we build on?

The best cloud AI platform is usually the one your data, identity and contracts already sit on: Microsoft Foundry for Microsoft 365 and Azure estates, Amazon Bedrock for AWS, the Gemini Enterprise Agent Platform for Google Cloud and BigQuery, IBM watsonx for regulated hybrid estates, and Huawei Cloud or Alibaba Cloud where regional presence matters. We compare the options on model choice, agent runtime, data location and cost before recommending one.

8. How much do enterprise AI development services cost?

Enterprise AI cost depends on the route (platform services, custom application or cross-SaaS integration), the number of systems connected, data preparation needed, security requirements and usage volume. Running costs for models, compute and storage sit separately from engineering fees. We scope one use case first and price the build once the baseline and architecture are agreed.

9. How long does an enterprise AI project take?

A first enterprise AI use case typically reaches production in eight to sixteen weeks: two to three weeks for scoping and readiness, four to eight weeks to build and integrate, and two to four weeks of evaluation and rollout. Projects built mainly on managed platform services sit at the shorter end; those connecting several SaaS platforms sit at the longer end.

enterprise AI development services that power your business systems