AI systems with evaluation, guardrails and human oversight.
BitAzure designs and builds AI applications, agents and automation on top of Claude and other model APIs: retrieval pipelines, tool-using orchestration, predictive models and computer vision, delivered with the evaluation suites, guardrails and observability that enterprise and financial systems require.
- LLM integration, AI agents and RAG systems
- AI automation, predictive analytics and computer vision
- AI for blockchain: on-chain analysis, anomaly detection, contract tooling
- Evaluation, guardrails, privacy and human-in-the-loop controls
What is AI development?
AI development is the engineering of software systems that use machine learning models, including large language models, to perform tasks such as answering questions over private data, automating multi-step workflows, classifying events, forecasting quantities or interpreting images. It includes model selection and integration, data and retrieval pipelines, orchestration and tool use, and the evaluation, safety and monitoring layers that make the behaviour measurable and controllable.
BitAzure builds AI systems for fintech, crypto, trading and enterprise clients. Typical deliverables are LLM-powered assistants and agents connected to internal APIs, retrieval-augmented generation over documents and databases, predictive models for risk and operations, computer-vision pipelines for document and identity workflows, and AI tooling for blockchain data such as anomaly detection and smart-contract analysis assistants.
We do not promise that a model will be right. We build systems where its outputs are evaluated against defined criteria, constrained by guardrails, escalated to people when confidence is low and observed continuously in production. That discipline is what separates a demo from a system a business can rely on.
AI development capabilities
Capabilities span application engineering and the operational practices that keep AI behaviour measurable after launch.
LLM Applications & Integration
- LLM integration with Claude and other model APIs
- AI applications: assistants, copilots and document workflows
- Prompt design, structured outputs and function calling
- Model routing, fallbacks and cost controls
- Streaming interfaces and long-context handling
AI Agents & Automation
- Tool-using agents with typed, permissioned tools
- Multi-step workflow automation with checkpoints
- Human-in-the-loop approvals for consequential actions
- Agent memory, state management and retries
- Back-office automation for support, operations and compliance
RAG & Knowledge Systems
- Retrieval-augmented generation over documents, tickets and databases
- Chunking, embedding and hybrid search strategies
- Vector databases with metadata filtering and access control
- Citation, grounding and freshness handling
- Evaluation of retrieval quality and answer faithfulness
Predictive Analytics & Computer Vision
- Forecasting and classification models for risk, churn and operations
- Feature pipelines and model training with PyTorch
- Computer vision for document, identity and image workflows
- Model serving with batch and real-time inference
- Drift monitoring and scheduled retraining
AI for Blockchain & Trading Infrastructure
- On-chain data analysis and entity labelling
- Anomaly detection for transfers, contracts and wallet behaviour
- Smart-contract assistant tooling for review and documentation
- Signal research tooling for quant teams, positioned as research support rather than guaranteed alpha
- Market surveillance and abuse-pattern detection
Enterprise AI Governance
- Evaluation suites with golden datasets and regression gates
- Guardrails: input filtering, output validation and policy checks
- Privacy controls, data residency and PII redaction
- Model and prompt versioning with audit trails
- Observability: traces, cost, latency and quality metrics
- Responsible-use documentation and access governance
Reference AI platform architecture
Applications never call models directly. A gateway handles credentials, routing and policy, an orchestration layer runs agents and retrieval, and every call passes through guardrails and evaluation before results reach users or systems.
Hover or focus a component to see its role in the architecture.
- Applications (Web · mobile · internal tools): Assistants, automation jobs and analytics products that consume AI capabilities through a single internal API.
- AI Gateway (Auth · routing · cost control): Holds provider credentials, routes requests by task and cost, applies rate limits and redacts sensitive input.
- Orchestration & Agents (Workflows · tool use): Runs multi-step workflows, manages agent state and calls models, retrieval and tools with typed interfaces.
- Human-in-the-Loop (Approvals · escalation): Review queues for low-confidence outputs and approval gates for consequential actions.
- LLM APIs (Claude · other models): Commercial and self-hosted models selected per task through the gateway abstraction.
- Retrieval Layer (Vector DB · hybrid search): Permission-aware retrieval over embedded documents and structured data with freshness tracking.
- Tools & Data (Internal APIs · on-chain data): Scoped connectors to business systems, indexers and chain data that agents can call.
- Guardrails (Policy · validation): Input filtering, schema validation of outputs, policy checks and injection defences on every call.
- Evaluation (Golden sets · regression): Automated evaluation against curated datasets, run on every prompt, model or retrieval change.
- Observability (Traces · cost · quality): End-to-end traces, token cost, latency and quality signals per feature and tenant.
Applications
- Assistants and copilots
- Automation workflows
- Analytics products
Gateway & orchestration
- Model routing and cost control
- Agents and tool use
- Human-in-the-loop
Models, retrieval & tools
- LLM APIs
- Vector search
- Internal and on-chain data
Control
- Guardrails
- Evaluation suites
- Observability
Where AI development delivers value
Support and operations assistants
Assistants grounded in internal knowledge that draft responses and escalate to people when unsure.
Document and KYC automation
Extraction, classification and verification of documents with review queues for exceptions.
On-chain intelligence
Entity labelling, anomaly detection and alerts for compliance and risk teams.
Smart-contract review tooling
Assistants that summarise, question and cross-check contracts to support, not replace, auditors.
Quant research tooling
Data preparation, feature exploration and experiment tracking for trading research teams.
Enterprise knowledge search
RAG over policies, tickets and systems with permission-aware retrieval.
Why teams build AI systems with BitAzure
Measurable behaviour
Every application ships with evaluation datasets and regression gates so changes are tested, not guessed.
Guardrails and escalation
Policy checks, output validation and human review paths for consequential decisions.
Model independence
A gateway that abstracts providers so models can be swapped or routed by task, cost and latency.
Domain depth
Engineers who understand blockchain data, trading systems and financial compliance, not only model APIs.
Privacy by design
Data minimisation, redaction and residency controls applied before any data reaches a model.
Production observability
Traces, cost and quality metrics per feature so AI spend and behaviour stay under control.
Technologies we use for AI development
LLM APIs
AIClaude and other model APIs for agents, RAG and automation.
Python
BackendData pipelines, quant research tooling and AI services.
TypeScript
FrontendStrict typing across the entire stack, client to contracts tooling.
Node.js
BackendEvent-driven APIs, gateways and real-time WebSocket services.
PyTorch
AICustom models for anomaly detection and forecasting.
Vector Databases
AIpgvector and dedicated stores for retrieval pipelines.
PostgreSQL
DatabaseTransactional ledgers, ledgers-of-record and analytical workloads.
ClickHouse
DatabaseColumnar analytics for trade history and market data.
Kafka
DatabaseDurable event streaming between engines and services.
Kubernetes
InfrastructureAutoscaling, rolling deployments and workload isolation.
AWS
InfrastructureEKS, RDS, KMS, CloudHSM and multi-region deployments.
Prometheus / Grafana
InfrastructureMetrics, dashboards and alerting for every service.
How we deliver AI projects
Every engagement follows a documented, milestone-driven path from discovery to continuous development.
- 01
Use-case and risk assessment
Identify tasks, success criteria, failure costs and data constraints, and decide where human review is mandatory.
- 02
Data and retrieval design
Sources, permissions, chunking and embedding strategies are designed and a golden evaluation set is assembled.
- 03
Prototype and evaluation baseline
A working prototype is measured against the golden set to establish baseline quality, cost and latency.
- 04
Orchestration, tools and guardrails
Agents, tool integrations, policy checks and escalation paths are built with typed interfaces and tests.
- 05
Hardening and observability
Tracing, cost controls, drift monitoring and privacy safeguards are added before production exposure.
- 06
Rollout and continuous evaluation
Staged release with production sampling feeding back into evaluation sets and model or prompt updates.
Security practices in AI development
Prompt injection defences
Input filtering, tool permissioning and output validation reduce the impact of untrusted content in prompts.
Data privacy
PII redaction, tenant isolation in retrieval and provider agreements aligned with data-handling requirements.
Least-privilege tools
Agents call tools through scoped credentials with allow-lists and dry-run modes for mutating actions.
Human-in-the-loop
Approval gates for financial, legal or irreversible actions with full context for the reviewer.
Model and prompt governance
Versioned prompts and models with change review, audit trails and rollback.
Monitoring and abuse detection
Rate limits, anomaly alerts on usage patterns and logging designed for investigation.
We coordinate independent third-party audits on request and never claim an audit, certification or compliance status unless it has actually been obtained.
What to decide before an AI project
Where AI adds value
Not every workflow benefits from a model; rule-based automation is often cheaper and more predictable. Start from the task, not the technology.
Accuracy expectations
Model outputs are probabilistic. Define acceptable error rates per task and design review paths for the rest.
Data readiness
RAG quality depends on source quality, permissions and freshness; data preparation is usually the largest part of the work.
Cost and latency
Token usage, context length and model choice drive cost; routing and caching strategies should be planned early.
Regulatory context
Financial and identity use cases may require explainability, record-keeping and human accountability for decisions.
Vendor dependence
Abstract model providers behind a gateway and keep evaluation sets so switching models is a measured decision.
AI Development questions
We integrate Claude and other commercial model APIs, and open-weight models where data residency or cost requires self-hosting. The gateway abstracts providers so the choice can change per task and over time based on evaluation results.
Evaluating an AI initiative?
Describe the task, the data and the cost of being wrong. We will propose an architecture, an evaluation approach and a phased plan.