PSYKHE | Technical advisory

Technical advisory

Get clear technical decisions, before costly commitments. This is decision-grade advisory. Focused, grounded, and designed to surface what’s ready, what’s risky, and what needs to change.

Capabilities

Our technical advisory offerings

AI data readiness assessment

A structured assessment of whether your data can realistically support your intended AI use cases. We look into the details that matter: availability, quality, access, governance, and risk.
Timeline: 2-3 weeks

Architecture advisory

An expert review of your current system landscape to assess whether it can support your product or platform goals. We focus on structure, integration, scalability, and long-term viability.
Timeline: 3-4 weeks

When technical advisory might be what you need

When you’re unsure if your foundations are ready to implement AI.

When architecture decisions are slowing delivery or increasing risk.

When leadership needs clarity before approving major investment.

When you want fewer surprises downstream.

What's included

Scope, timelines, and what you’ll walk away with

Indicative pricing

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What you get:

Data readiness assessment summary.

Identified gaps and risks that affect AI feasibility.

Clear recommendations on what's required before AI implementation.

Priority actions to move from current state to AI-ready.

Assumptions:

Scoped to a defined set of intended AI use cases, agreed upon up front.

Focused on a limited number of core data sources. The exact depth depends on the number of data domains and systems in scope.

Data engineering or model development not included.

Week 1

Align on intended AI use cases, goals, and decision context.
Identify critical data domains, sources, and owners.
Review data access, pipelines, and current data flows.

Week 2

Assess data quality, coverage, and usability for AI.
Evaluate governance, security, privacy, and compliance constraints.
Identify risks, dependencies, and readiness gaps.

Week 3

Prioritise actions needed to move from current state to AI-ready.
Define practical recommendations and sequencing.
Consolidate findings into a readiness summary and action plan.

Indicative pricing

Ask us

What you get:

Architecture assessment and risk overview.

Identified constraints and technical debt hotspots.

Target-state architectural recommendations.

Practical guidance on what to fix, evolve, or avoid.

Assumptions:

Scoped to an agreed system landscape or platform.

Focused on current-state architecture and near-term future needs.

Solution design and/or implementation not included.

Week 1

Onboard and align on business objectives and technical context.
Map current architecture, systems, and core dependencies.
Identify known constraints and areas of concern.

Week 2

Review integrations, data flows, and system boundaries.
Assess scalability, resilience, and maintainability.
Identify architectural risks, bottlenecks, and technical debt hotspots.

Week 3

Evaluate future readiness against product, platform, or AI ambitions.
Define target-state architectural principles and guardrails.
Outline recommended changes and trade-offs.

Week 4

Refine recommendations with stakeholders.
Prioritise what to fix, evolve, or avoid.
Finalise architectural guidance and decision inputs.

Your team

Who you'll have on the team

Technical Lead or Solutions Architect

To handle your audit from end-to-end. They dig through your flows, score attributes, benchmark against competitors and provide you with their findings and recommendations.

AI Specialists

To support AI-related assessments where data pipelines, models, or infrastructure are involved.

FAQs

Questions we frequently get

Who conducts the technical assessments?

We coordinate technical advisory through trusted delivery partners with deep expertise in architecture and data systems.

PSYKHE manages the engagement, translates findings into business context, and ensures recommendations are practical and actionable.

This way, you get the benefit of specialised technical depth without needing to vet and coordinate multiple vendors yourself.

How do we know if we need this now or if we should wait?

If you're about to commit budget to AI implementation, a major build, or platform migration, do it now. If you're still in early exploration or have years before execution, you probably don't need it yet. The question is: are you making decisions that are hard to reverse? If yes, get clarity first.

What happens after the assessment? Will we actually know what to do next?

You’ll have prioritised actions with clear rationale. If you need help implementing, we can coordinate that through our delivery partners. If your team can execute, you'll have what you need to move forward confidently.