000AI SERVICES

Engineering services for AI systems that must survive operations.

We connect mission and technology strategy, agentic systems, data and platform integration, edge deployment, test and evaluation, post-quantum migration, quantum engineering, and capability transfer. Every engagement starts with constraints and acceptance evidence—not a generic transformation promise.

Mission and technology strategyAgentic systems with human authorityEdge AI and systems integrationPQC migration and quantum engineering
Discuss an operating problemExplore services
Concept operating-context map connecting decision intake, governed intelligence workflows, edge runtime, quantum evidence, and production handoffConcept architecture

Delivery phases

At every gate

Built into handover

Business team reviewing printed analytics and strategy documents in a professional office

A delivery model for AI and quantum programs where users, data, authority, interfaces, tests, and operating ownership must stay connected.

Acceptance criteria
Evidence at each gate
Customer capability transfer
Live model
1

Discover

Threat model

2

Architect

Human approval

3

Deploy

SLA operations

4

Operate

Threat model

Signal

Threat model

Signal

Human approval

Signal

SLA operations

001Core delivery services

Core services cover the path from mission framing and architecture through integration, test and evaluation, deployment, operations, and capability transfer. Scope is set by the target environment and acceptance evidence—not a generic package tier.

Enterprise operating context map connecting executive decisions, governed AI workflows, edge systems, and quantum evidenceProduct interface
01

Turn operating priorities, data readiness, risk, and technology maturity into a costed roadmap with explicit investment gates.

Scope and deliverables
NowFlow workflow builder with connected agent, approval, integration, and audit nodesProduct interface
02

Design approval-gated AI workflows that connect models, tools, operators, data, and evidence across real systems.

Scope and deliverables
Technician configuring edge infrastructure hardware inside a network rack
03

Benchmark, package, harden, and observe AI workloads for devices with constrained compute, power, or connectivity.

Scope and deliverables
Enterprise data center corridor representing secure AI integration infrastructure
04

Connect data, identity, APIs, sensors, enterprise platforms, and operational systems through testable interfaces.

Scope and deliverables
Team planning governance controls and implementation milestones on a project board
05

Define test and evaluation, human oversight, traceability, release gates, failure handling, and model-risk controls.

Scope and deliverables
Modern data center corridor representing the infrastructure, monitoring, and controlled change behind managed AI operations
06

Operate models, workflows, and edge releases through agreed telemetry, incident, change, and governance processes.

Scope and deliverables
002Quantum engineering

Post-quantum security is an engineering migration. Quantum computing and sensing work is an evidence-led research program. We keep those maturity levels and budgets distinct.

Quantum-safe policy and migration context connecting U.S. and European timelines, PQC migration, and review evidenceConcept visualization
07

Inventory cryptography, prioritize long-lived exposure, plan PQC migration, assess QKD boundaries, and test rollback paths.

Scope and deliverables
QFlow Studio quantum workflow canvas showing circuit nodes, provider context, and execution controlsProduct interface
08

Frame quantum experiments with classical baselines, provider context, resource estimates, reproducible runs, and decision evidence.

Scope and deliverables
Quantum AI workflow evidence board with hybrid execution, baselines, resource estimates, and decision recordsConcept visualization
09

Evaluate hybrid quantum-classical methods against credible baselines before a research program or hardware budget scales.

Scope and deliverables
Specialist research contexts

These are not off-the-shelf quantum capabilities. They are sector-specific framing, baseline, protocol, resource-estimation, security, and decision-evidence engagements.

Quantum telecom infrastructure with radio tower, fiber links, secure appliance, and evidence panelsResearch context

10

Connect crypto-agility, QKD fit, network dependencies, edge rollout constraints, and operator evidence in one program.

Review scope →

Quantum finance operations room with risk dashboards, portfolio analytics, quantum evidence, and governance panelsResearch context

11

Evaluate optimization and simulation candidates with classical baselines, model-risk controls, resource estimates, and PQC exposure.

Review scope →

Quantum defense operations lab with multidomain map, quantum sensor records, secure communications, and approval timelineResearch context

12

Structure public-safe research around sensing, resilient PNT, secure communications, optimization, simulation, and field robustness.

Review scope →

Quantum health laboratory evidence workflow with sensor hardware, biological sample, molecular model, and review panelsResearch context

13

Structure sensing and biomedical simulation research around protocols, calibration, baselines, privacy boundaries, and uncertainty.

Review scope →

002How we work

Discovery, design, build, and operate are connected, but not automatic. Each phase ends with named artifacts, owners, acceptance tests, and an explicit go, revise, or stop decision.

Timing is scoped after discovery · production cutover requires acceptance
01Phase 01
2–4 weeks

Scope goals, audit data readiness, and align stakeholders around a prioritised AI roadmap. We finish discovery with a costed roadmap, an investment scorecard, and an explicit list of what won't ship.

Phase record

Deliverables

  • Investment scorecard with ROI estimates
  • Costed roadmap (0–6 / 6–12 / 12+ months)
  • Data + integration readiness audit
  • Risk register + governance baseline

Phase gates

Executive sponsor sign-offData residency + access plan agreedScope of phase 2 contract drafted

Owners

AI ArchitectCustomer SMEsExecutive sponsor
02Phase 02
3–5 weeks

Architect the workflow / edge model / integration. Threat-model security and compliance up front. Sign off the deployment plan with the customer's CISO and SRE before any code touches production.

Phase record

Deliverables

  • Reference architecture + sequence diagrams
  • Threat model (STRIDE) + compliance crosswalk
  • Test + acceptance plan
  • Pilot scope + success metrics

Phase gates

Security + privacy review passedArchitecture review board approvalPilot acceptance criteria signed

Owners

AI ArchitectCustomer CISOSRE / platform team
03Phase 03
4–8 weeks

Implement the agreed workflow, edge image, model, data path, or integration and test it against the signed acceptance plan. Release identity, configuration, test results, and known limitations travel together.

Phase record

Deliverables

  • Workflow / model / image releases
  • End-to-end integration tests
  • Runbooks + on-call playbooks
  • Pilot deployment to first cohort

Phase gates

Integration acceptance test passedPilot success metrics on trackCutover plan approved

Owners

AI EngineersCustomer integration teamQA
04Phase 04
Ongoing

Operate the system under an agreed support model with named telemetry, incident paths, change controls, model or data checks, and review cadence. Coverage and response targets are contracted for the actual deployment.

Phase record

Deliverables

  • Monitoring + on-call model agreed in scope
  • Performance + governance review cadence
  • Model + drift watchers
  • Continuous-improvement backlog

Phase gates

Monthly SLA reportQuarterly business reviewAnnual governance + risk attestation

Owners

Managed AI Ops teamCustomer ops lead
003Why Neura Parse
01

Before implementation, we define users, failure modes, authority, data boundaries, test cases, and the evidence required to accept a release.

02

Architecture, implementation, integration, runbooks, and capability transfer stay connected so the customer can operate what is delivered.

03

Product-backed work, integration, prototypes, and research are labelled separately; numerical performance is established on the target system.

Start the program

Bring the operating environment, users, data boundaries, integration points, and acceptance question. We will help determine whether the right next step is architecture, a bounded pilot, a migration plan, or research.

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