AI Solutions & LLM Engineering | Vortex Web Innovate

Problem

Why do enterprise AI applications fail to move from pilot to production?

A pilot is successful, but the celebration can often be short-lived since a pilot rarely addresses the four key questions that determine if and how an AI application can transition into production: can quality be measured, can costs be controlled, can governance requirements be met, and can the system truly respond to operational demands? Here are five essential stages for passing the pilot test:

Evaluation

“It passed the test” is not a quality assurance standard. Without defined sets of evaluation checkpoints and regression analysis, you cannot identify opportunities to improve accuracy or distinguish between a tweak that improves performance versus one that diminishes it

Consumption

While a beta can handle a few hundred queries, production-ready AI applications must deliver at scale. Without the right spend, storage, and model routing allocated, costs can balloon out of control if the organization needs to handle millions of transactions

Governance

What documents are used to formulate the response? Can you demonstrate that the user has access to these documents? Can you delete a customer's data from the list of documents used to formulate a response? If you cannot answer these questions, your system will fail governance and legal audits.

Reliability

Model providers can change, their behaviors can change, and they can cut you off at any time. If you have no alternatives and no built-in redundancy, you cannot respond to issues quickly enough if the API provider decides to change how it operates.

Integration

The value of an AI system needs to be measured not in isolation but in how it is connected to the broader system: ERP, CRM, ticketing, document management, and permissions.

What We Offer

RAG Systems for Your Proprietary Data

We design processes that allow you to analyze and classify your documents according to your specific content categories using hybrid search, re-ranking, and citation capabilities. Permission-based search ensures users see only the data they are allowed to consume.

LLM Assistants

We can design and implement internal and external AI assistants that generate, summarize, categorize, route, and extract structured data from unstructured documents. We can transcribe annotations, validate results, and implement backup options for your documents.

AI Agents and Workflow Automation

We can implement AI systems that extend beyond content creation. An AI agent can execute commands such as filing a document, updating a database record, or triggering downstream processes based on permissions, approvals, and audit trails for sensitive requests.

Document Intelligence

We can implement OCR and document-processing systems that extract structured data from reports, invoices, claims, contracts, and other documents. Confidence thresholds and multi-tiered human reviews allow for the appropriate triage of complex items.

Computer Vision

We can design computer vision systems that identify, recognize, count, and classify items in video and imagery. Our software can run in the cloud or on-premises and at the edge.

Predictive Models

We can build predictive models, risk, and hazard models, recommendation systems, and anomaly detection systems. We can also implement feature pipelines that allow these models to run in production.

Evaluation and Observability

We can implement golden datasets, regression testing, anomaly detection, human feedback loops, and dashboards that tie quality, latency, and cost to features.

Model Operations (MLOps / LLMOps)

We can replicate models, declarations, data, and deploy changes incrementally so you can always rewind to the previous version if something goes wrong. Model routing lets you switch providers or use open source models when needed.

Governance is a design imperative

Governance is a build requirement, not a policy document

Enterprise AI dies in legal review more often than in engineering. We build for that review from the start. Many enterprise AI initiatives face their toughest regulatory and security hurdles not during development but during critical design reviews. We design our systems with this reality in mind.

Data Replacement

We can use data in your cloud environment to fulfill your use cases in Azure OpenAI, AWS Bedrock, or Google Vertex AI and keep your data and challenges inside your environment.

Data Uploading with Permission Respect

AI Insights respects permission on your systems.

Audit Log

We can log claims, sources, forms, and products for auditing purposes and allow data subjects to review this information.

Personal Information (PII) Management

We can identify and withhold personal information from use in accordance with the contract.

Privacy Controls

We can implement input filters, entity limits, action confirmations, human-gating review processes, or other methods.

Right of Erasure

We can delete inventory and storage information as appropriate in response to a data subject's exercise of rights.

Model Transparency

We can provide model selection, versioning, and rollback capabilities for any AI function.

Regulatory Requirements

We can implement the technical processes and documentation required by law, including the EU Artificial Intelligence Act (AI Act), HIPAA, the GDPR, and other regulations governing health care, finance, and other industries. We can advise your legal team; we do not give legal advice.

How does an engagement work?

How an AI engagement runs

AI Readiness Assessment · 2 weeks

We analyze your data, security requirements, and potential use cases. Each opportunity is evaluated on technical feasibility, commercial viability, and governance risks. You will get a prioritized shortlist of opportunities, including an objective assessment of ideas that may not be worth pursuing.

Design and Evaluation · 2-3 weeks

We define success metrics before designing the AI feature. We design a comprehensive set of assessments, define target quality levels, and agree on the cost budget.

Build · Typically 6-12 weeks

We iteratively design the research, coordination, and control assets needed for production while collecting user feedback every two weeks. By building development around the assessment package, we ensure that quality is measurable and thus achievable.

Test with Real Users

We release the feature to a select group of users with real-world queries. We collect feedback, measure quality, and track performance weekly. We define success and exit criteria and get agreement on them before test launch.

Launching Production

We can help you with the phased production rollout and associated cost control, variance analysis, operating book production, and incident response processes.

Operations and improvements

AI systems are iterative, and operations should be designed to constantly evolve as the models, data, and use cases change. Our aim is to continually improve your systems by updating the challenges, data, and research relevant to the task at hand.

Core Model Selection

Technology stack

We have no inherent bias for one core model as we have no commercial interest in any model other than satisfying our customers' requirements. In practice, we find that there is rarely one model that dominates in terms of quality, speed, and cost; if there is, we will tell you. We believe that abstraction layers should be built on top of models to ensure that you are not married to one model or vendor. Wherever possible, we use open source models to avoid vendor lock.

Models: OpenAI

Anthropic Claude

Google Gemini

Meta Llama

Mistral

Azure OpenAI

AWS Bedrock

Google Vertex AI

These and other models are accessible through Azure OpenAI, AWS Bedrock, and Google Vertex AI when data residency is a requirement.
Orchestration: LangChain

LlamaIndex

LangGraph

Model Context Protocol (MCP)

custom
Vector search: pgvector

Pinecone

Qdrant

Weaviate

Elasticsearch

Azure AI Search
ML: PyTorch

TensorFlow

scikit-learn

Hugging Face
Computer Vision: OpenCV

YOLO

cloud vision APIs
Operations: MLflow

Weights & Biases

LangSmith

OpenTelemetry
Data: Snowflake

Databricks

BigQuery

Airflow

DBT

Kafka

Where can value be delivered from enterprise AI technologies?

Where enterprise AI is currently paying for itself

Internal Knowledge Assistants

AI assistants that research and analyze company policies, contracts, documents, and other information while respecting permissions.

Employee Support

AI systems that use your knowledge base to recommend or generate answers to resolve employees' queries.

Document Processing

AI-powered claims processing, invoice review, registration, and other processes that require document-intensive workflows under human oversight.

Sales and Revenue Intelligence

Contact analysis, enrichment of CRM data, and preparation of sales presentations.

Engineering Acceleration

Code review, test generation, documentation, and other engineering acceleration tools for legacy and modern codebases.

Operations

Prediction, anomaly detection, quality assurance, and predictive maintenance.

FAQ

Frequently Asked Questions

Yes. We can deploy Azure OpenAI, AWS Bedrock, Google Vertex AI inside your environment or use open source models in your preferred infrastructure. Your choice of solution depends on your requirements in terms of quality, security, and regulation.

We rely on trusted knowledge sources, cite them when we can, and build verification layers for structured products when needed. We define the scope of what the system can say no to. But most importantly, we use regression analysis, so you can see the quality and quantify the loss when you make changes

We define the cost during the design process and then establish a cost ceiling, below which implementation begins. We use caching, routing, fast compression, and other methods to get underneath the cost ceiling. We can also analyze costs by function and tenant and correlate costs with quality and performance.

The assessment takes about two weeks, and depending on the complexity of use cases, we can get to production within a timeframe you define. In many enterprise AI projects, data preparation often takes much longer than model construction.

Our solutions are designed with enough abstraction layers and evaluation coverage that you could replace the provider at the lowest practical level. This reduces the burden on other functions and limits production interruptions when a provider exits or downgrades a model.

When it is commercially viable and technically feasible. For many applications, fine-tuning the recovery generation can deliver far better ROI than training. We believe that fine-tuning should not be the default, and we will only choose it if we have evaluated and demonstrated its value.

Building AI for Production, Not Demo

Enterprise AI is much more than a compelling proof of concept; it must be produced with production-level quality, cost control, security, and reliability. Vortex Web Innovate is an organization that creates enterprise AI applications that meet these requirements and enable organizations to transform interesting ideas into products that are fit for purpose and production-ready.

Start with an AI readiness assessment

Two weeks. We review your data, your constraints, and your candidate use cases, then hand you a ranked shortlist with feasibility, governance risk, and a cost model — including which ideas we'd advise you not to build.

Book a Technical Discovery Call Download the AI Readiness Framework