Technical Evaluation
Technical evaluation guide
Architecture, deployment models, security controls, and an honest comparison against alternatives. Everything a CTO or Head of IT needs for due diligence.
Architecture
Four layers, cleanly separated
CompleteFlow separates AI agents, workflow execution with document processing, governance, and infrastructure into independent layers. Each has a well-defined interface and can be modified or extended without affecting the others.
AI Agent Layer
Conversational AI agents handle the work that requires judgement: interpreting documents, answering questions, reasoning across multiple sources. Agents choose which tools and workflows to invoke based on the task.
Workflow & Document Processing Layer
Deterministic workflows for tasks that must execute identically every time. Document ingestion, extraction, cross-referencing, and validation. Expression-based data transforms. Agents invoke workflows as tools, and workflows can call agents for steps that need reasoning.
Governance Layer
Workgroup-based access control with default-deny policies. Full audit trail on every agent action, tool call, and workflow step. Reasoning traces show how the AI reached each conclusion.
Infrastructure Layer
Deploys on your chosen infrastructure using managed services. Multi-provider LLM access via private endpoints (Azure OpenAI, Anthropic, open-weight models). PostgreSQL with pgvector for data and vector search. Azure Key Vault for secrets and per-user credentials. All components containerised with infrastructure-as-code.
Deployment
Three deployment models, each with clear trade-offs
The right deployment model depends on your regulatory constraints, existing infrastructure, and operational capacity. Here is exactly what each option involves.
Private Cloud
Typical timeline: 4-6 weeks
Infrastructure
Your Azure, AWS, or GCP tenancy
Networking
Hub-spoke VNet/VPC with private endpoints. No public internet exposure for data plane. Management plane accessible via VPN or private link.
Compute
Azure Container Apps, AWS ECS/Fargate, or GCP Cloud Run. Auto-scaling based on agent workload.
LLM Providers
Works with any state-of-the-art model: Anthropic Claude, OpenAI, Google Gemini, Azure OpenAI, and open-weight models (Llama, Mistral) in your tenancy. Commercial API tiers. Your data is never used for model training.
Data Layer
PostgreSQL managed service (Azure Database for PostgreSQL, RDS, Cloud SQL) with customer-managed encryption keys.
Identity
Microsoft Entra ID with delegated OAuth. Agents inherit requesting user permissions.
Monitoring
OpenTelemetry to Azure Monitor, CloudWatch, or your existing SIEM.
On-Premises
Typical timeline: 6-10 weeks
Infrastructure
Your own hardware or private data centre
Networking
Fully air-gapped. No outbound internet required. All components run within your network perimeter.
Compute
Docker Compose for single-node. Kubernetes (any distribution) for multi-node. Bare metal or VM deployment.
LLM Providers
Open-weight models only (Llama, Mistral). Runs on your GPU infrastructure. No external API calls.
Data Layer
Self-managed PostgreSQL with your encryption and backup policies. Full control over data lifecycle.
Identity
On-premises Active Directory or SAML 2.0 identity provider. Local auth fallback available.
Monitoring
OpenTelemetry export to your existing monitoring stack (Grafana, Splunk, ELK).
CompleteFlow Cloud
Typical timeline: 2-3 weeks
Infrastructure
Private cloud managed by Atchai (deploy in any region)
Networking
Dedicated tenant environment. Data residency in your chosen region guaranteed. SOC 2 controls applied.
Compute
Managed container infrastructure with automatic scaling and patching.
LLM Providers
Anthropic Claude, OpenAI, and Azure OpenAI. Commercial API tiers. Your data is never used for model training. Model selection per agent.
Data Layer
Managed PostgreSQL with encryption at rest. Data residency in your chosen geography.
Identity
Microsoft Entra ID SSO. Federated identity with your existing directory.
Monitoring
Built-in dashboard with agent metrics, cost tracking, and audit log viewer. Export to your SIEM available.
Security Model
Defence in depth, not perimeter-only
Security controls are applied at every layer: network, identity, encryption, policy, and audit. No single control is a single point of failure.
| Domain | Control | Detail |
|---|---|---|
| Single-tenant architecture | Dedicated deployment per client | No shared infrastructure, no shared database, no co-tenancy. You own the Azure subscription and can revoke access at any time. |
| LLM data privacy | Private LLM endpoints, zero training on your data | Azure OpenAI via private endpoint within your subscription. Prompts and completions never used for model training by Microsoft or OpenAI. |
| Per-user credentials | OAuth delegation with Key Vault storage | Each user authenticates separately with external services. Tokens stored in Azure Key Vault (hardware-backed). Agents see only what the authenticated user can see. |
| Encryption | AES-256 at rest, TLS 1.2+ in transit | Customer-managed keys available. HS256 signed access tokens. SHA-256 hashed API keys. SSL required on all database connections. |
| Network isolation | Private endpoints, no public data plane | Database, Redis, and LLM accessed via private networking only. External ingress limited to web app and Teams bot. |
| Access control | Workgroup-based RBAC with default-deny | Per-workgroup roles (owner, contributor, reviewer, viewer). AI tool access filtered by user permissions. No default access for new users. |
| Secrets management | Azure Key Vault with managed identity | All secrets in Key Vault. No secrets in code, config, or environment variables. Only the credential broker service can access tokens. |
| Audit logging | Full provenance chain, 7-year retention | Every agent action, tool call, workflow step, and approval decision logged with who, what, when, and cost. Reasoning traces exportable. |
| Container security | Hardened base images, CI/CD scanning | CIS-benchmarked container images (CBL-Mariner). Dependency vulnerability scanning and SAST on every build. CVE scanning on images. |
| Disaster recovery | Azure managed backups with geo-redundancy | 35-day automated backup retention. Point-in-time restore. Geo-redundant storage. Optional multi-region failover. |
| Data residency | Deploy in any geography | US, UK, EU, or any region. Data does not leave the designated geography. All processing within your subscription. |
Compliance Mapping
How CompleteFlow maps to regulatory frameworks
Compliance is not a feature that gets added later. These controls are built into the agent execution pipeline. Below is a mapping of specific regulatory requirements to the CompleteFlow controls that address them.
FCA Consumer Duty
| Requirement | CompleteFlow Control |
|---|---|
| Demonstrate outcomes monitoring | Immutable audit trails capture every AI decision with reasoning traces, confidence scores, and outcome data |
| Evidence of fair treatment | Output logging for bias review and audit. Human review gates for high-stakes decisions |
| Explainability of AI decisions | Reasoning traces export showing tool calls, data sources, confidence scores, and the decision path for each output |
| Governance and oversight | Workgroup-based access control with audit logging. Configurable human-in-the-loop approval gates |
SRA Standards and Regulations
| Requirement | CompleteFlow Control |
|---|---|
| Client data confidentiality | Private deployment on firm infrastructure. Delegated OAuth ensures agents only access what the requesting user can access |
| Competence and supervision | Human-in-the-loop review for all client-facing outputs. Confidence thresholds trigger mandatory review |
| Record keeping | Two-level audit logging with 7-year retention. Every agent interaction recorded with full context |
| Technology risk management | Version-controlled agent configurations. Rollback capability. Sandbox testing before production deployment |
MiFID II
| Requirement | CompleteFlow Control |
|---|---|
| Transaction reporting | Per-agent, per-user activity logging with timestamps. Exportable audit records in standard formats |
| Best execution documentation | Reasoning traces capture the full decision path including data sources consulted and alternatives considered |
| Algorithmic trading controls | Rate limits, circuit breakers, and mandatory human gates for actions above configurable thresholds |
| Record retention (5 years) | Configurable retention periods. Default 7-year retention exceeds the MiFID II 5-year requirement |
GDPR
| Requirement | CompleteFlow Control |
|---|---|
| Data minimization | Agents process only the data required for each task. No persistent caching of personal data beyond the task lifecycle |
| Right to erasure | Tenant-level and user-level data deletion with cascade. Audit records anonymised (not deleted) to preserve regulatory trail |
| Data portability | Full data export in standard formats. No proprietary data lock-in |
| Breach notification | Automated anomaly detection with alerting. Incident response procedures with 72-hour notification support |
EU AI Act
| Requirement | CompleteFlow Control |
|---|---|
| Risk classification | Agent risk tiers mapped to EU AI Act categories. High-risk agents receive additional governance controls automatically |
| Transparency obligations | Users are informed when interacting with an AI agent. Reasoning traces provide full transparency of AI decision-making |
| Human oversight | Configurable human-in-the-loop gates. High-risk decisions require explicit human approval before execution |
| Technical documentation | Automated generation of technical documentation per agent: capabilities, limitations, training data lineage, and performance metrics |
Integration Architecture
MCP-first, not API-spaghetti
CompleteFlow uses the Model Context Protocol (MCP) as its primary integration standard. MCP is the open standard for connecting AI agents to external tools and data sources. Any system with an MCP server can be connected to CompleteFlow agents without custom integration code.
Integration via MCP
Each external system connects via a tool interface that the agent discovers at runtime. Tools run within your network perimeter. No data leaves your deployment environment during tool calls. Adding a new integration does not require changes to agent code.
Per-user credential delegation ensures each user's agent uses their own OAuth tokens when accessing external services. The external system's own permissions (folder access, ethical walls, matter restrictions) are enforced at the source.
Microsoft 365 Integration
Microsoft 365 has dedicated support via the Graph API with delegated OAuth tokens. When a user asks an agent to search SharePoint or access OneDrive, the agent uses that user's existing permissions. No admin consent for broad access required.
Agents surface in Microsoft Teams as a chat bot. Users interact through natural language in the same environment they already work in.
Comparison
CompleteFlow vs the alternatives for regulated environments
Four approaches to deploying AI in regulated industries. Each has trade-offs. This table presents the differences without editorialising. The right choice depends on your constraints, budget, and timeline.
| Criterion | CompleteFlow | DIY (Open Source) | SaaS AI Tools | Palantir AIP |
|---|---|---|---|---|
| Time to production | 6 weeks | 12-18 months | 2-4 weeks | 6-12 months |
| Initial cost | From £40k | £200k-500k+ (team cost) | £20-1,000/user/month | £1M+/year |
| Data residency | Your infrastructure, any geography | Your infrastructure | Vendor cloud (usually US) | Your infrastructure |
| Audit trail depth | Two-level immutable logging, 7-year retention | Must build from scratch | Basic execution logs | Enterprise audit logging |
| Access control | Workgroup-based RBAC, default-deny | Must build or integrate | Role-based only | Proprietary policy engine |
| Human-in-the-loop | Native gates with suspend/resume | Must build | Limited or none | Available |
| Reasoning traces | Every decision, exportable | Must instrument | Rarely available | Available |
| LLM flexibility | Multi-provider, swap without code changes | Full flexibility (if built) | Vendor-locked model | Multi-provider |
| Regulatory mapping | FCA, SRA, MiFID II, GDPR, EU AI Act | Must map yourself | GDPR only (if any) | Enterprise compliance |
| Vendor lock-in | Low. Open-source stack, full data export. Migration requires re-integrating workflows | None | High | High |
| Ongoing team required | Minimal. We manage the platform; your team manages agents | 2+ FTE AI/platform engineers | 0.5 FTE | 2-4 FTE + Palantir engineers |
Evaluation Checklist
What to look for in an AI agent platform
Use this checklist to evaluate CompleteFlow, competitors, or an in-house build. These are the questions that separate platforms built for real document work from wrappers around an LLM.
Document Processing
- ▢ Can the platform ingest PDFs, Word docs, emails, and spreadsheets?
- ▢ Does it extract structured data into tables, or just summarise?
- ▢ Can it cross-reference figures and statements across multiple documents?
- ▢ Is there vector search (RAG) across your document corpus?
- ▢ Can it generate new documents from extracted data and templates?
AI Agent Capabilities
- ▢ Can agents reason across document bundles, not just retrieve from them?
- ▢ Do agents invoke deterministic workflows when precision is needed?
- ▢ Can every team member access the agent through a conversational interface?
- ▢ Are agent responses traceable back to source documents with references?
- ▢ Can you swap LLM providers without rewriting agent logic?
- ▢ Can you choose different models per workflow (e.g. fast/cheap for extraction, powerful for reasoning)?
Workflow Engine
- ▢ Is there a visual builder and an expression language for complex logic?
- ▢ Are workflows idempotent (safe to rerun without duplication)?
- ▢ Does the engine support human-in-the-loop gates with suspend/resume?
- ▢ Can workflows run on a schedule or be triggered manually?
- ▢ Can agents call workflows as tools, and can workflows call agents?
Security and Access
- ▢ Does the platform deploy on your infrastructure (not SaaS-only)?
- ▢ When an agent accesses external systems, whose credentials does it use? Per-user or shared?
- ▢ Where do LLM API calls terminate? In your tenancy or the vendor's?
- ▢ Is there a full audit trail with reasoning traces, or just execution logs?
- ▢ Does the access control model support per-team/per-matter isolation?
Operations and Ownership
- ▢ Do you own the agents and workflows that are built? Can you export them?
- ▢ What does the platform require from your team to operate day-to-day?
- ▢ Is there version control and rollback for workflow definitions?
- ▢ What is the cost model? Per-agent cost tracking and attribution?
- ▢ What is the time to first production deployment?
FAQ
Technical evaluation questions
What types of documents can CompleteFlow process? +
How do agents access our document management system? +
Can we choose which LLM to use? +
What deployment options are available? +
What happens if we want to leave? +
How long to get to production? +
Do we need AI expertise in-house? +
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