Consulting / AI consulting
AI consulting from a team that ships AI.
An AI consulting company that also builds: which use cases to start with, how much autonomy each task deserves, and whether to build at all, grounded in AI systems we run in production.
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What AI consulting covers. The decisions that shape an AI initiative, made before the build.
AI readiness
An honest read on whether AI is the right tool for the workflow in question.
Explore →Use-case selection
Which tasks to start with, based on value, data and risk.
Data readiness
Whether the data exists, can be reached and is good enough to build on.
Build versus buy
Custom development compared with existing tools for your case.
Model choice
Models evaluated against your real workflow, with an architecture that lets them change later.
Workflow, agent or hybrid
A fixed workflow where it is enough, agents only where reasoning adds value.
Governance by design
Human approval checkpoints, audit trails and monitoring planned from the start.
Cost and effort
A realistic view of build and running costs, including model usage.
Roadmap
A phased plan, starting with the step that proves value fastest.
Match the autonomy to the task. The core of our AI advice: not every task needs an agent.
- L1WorkflowA fixed, auditable path.
- L2AssistantAI drafts. A person reviews.
- L3Supervised agentAI acts and flags exceptions.
- L4AutonomousRoutine, low-risk, monitored.
- Human-ledPeople decideAI supports the analysis.
Routine data entry and mapping
A fixed, auditable workflow is usually enough.
L1 WorkflowDrafting documents and replies
AI drafts. A person reviews and sends.
L2 AssistantEvidence-heavy review
Agents check every item and flag exceptions for people.
L3 Supervised agentRecurring low-risk checks
Can run on their own, with monitoring.
L4 AutonomousJudgement and approvals
Stay with people. AI supports the analysis.
Human-led
Delivered work behind our advice. Our recommendations draw on systems we have built. These are delivery engagements, not consulting projects.
AI agent platform
Agents retrieve, reason and act across systems, with human approval before high-stakes actions.
~40% less manual effort AI · Document intelligenceAI document intelligence
Extraction and classification that sends only the exceptions to people.
>80% less manual review AI · InsuranceFull-stack AI platform for insurance
Fragmented policy, claims and correspondence data connected into one structured workflow.
~2.5x faster processingNot sure where AI will pay off?
How an engagement runs. Four steps, ending in a recommendation you can act on.
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Intake
Understand the specific decision you are trying to make.
Intake notes
- The decision to make
- Constraints and timeline
- Who needs to sign off
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Assessment
Readiness, architecture or regulatory review, scoped to that question.
Assessment
- Current state reviewed
- Options compared
- Risks identified
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Recommendation
A clear, honest recommendation, including "do not build this yet" when that is the answer.
Recommendation
- Clear recommendation
- Trade-offs explained
- Rough cost and effort
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Handoff or build
A written handoff to your team, or a direct move into a delivery engagement.
Next step
- Written roadmap
- Architecture outline
- Optional delivery plan
Why teams choose us for AI advice. Advice from people who ship.
Honest, not sales-driven
We will say "do not build this yet" when that is the accurate read.
Same team, start to finish
The team that assesses readiness is the team that would build it.
Grounded in delivered work
Advice informed by AI and Web3 systems we have shipped, not theory.
Regulation-aware
Compliance considerations are part of the advice, not a separate afterthought.
AI and Web3 under one roof
One team that can compare both, rather than favouring the one it sells.
Clear written output
Every engagement ends with a recommendation you can act on or hand over.
Engagement models. Pick the shape that fits where you are.
Scoped assessment
A focused assessment and written recommendation, with no build obligation.
Same team throughout
The advisory phase moves straight into a project-based or dedicated-team build.
Recurring input
A standing relationship for teams that need regular architecture or regulatory input.
What our clients say.
AI platform for an insurance broker
“Avinya Labs built our full-stack AI solution … Their expertise accelerated our launch and significantly reduced execution risk.”
Guides on this topic. Plain answers to the questions buyers ask first.
Enterprise AI Deployment: Why Projects Stall in Pilot
The eight causes we see most, and a roadmap that gets AI live.
Read the guide → GuideOperational AI Systems: A 2026 Guide for Enterprises
How AI moves from pilots into the daily operations of a business.
Read the guide → GuideRAG vs Fine Tuning: Which Does Your Business AI Need?
When to use each, when to combine them, and how cost and upkeep compare.
Read the guide →AI consulting FAQs.
What do we get at the end?
A written recommendation: which use cases to start with, the architecture outline, the level of autonomy for each task, and a rough cost and effort view.
What if AI is not the right answer?
Then we say so, with the reasons. Sometimes a simple workflow does the job.
Do we have to build with you afterwards?
No. The recommendation is yours to take to your own team or another partner.
Which AI models do you use?
We evaluate models against your real workflow and choose per task. The architecture is designed so models can change later without rebuilding the product.
How do you handle our data?
Access is scoped to what the system needs, with role-based permissions and audit trails where records change. We agree data handling during discovery.
How do you choose an AI consulting company?
Look for plain recommendations, including when not to use AI, and a plan you can run without the consultant. Our AI consulting company approach and AI consulting services give you that, with risks assessed using the NIST AI Risk Management Framework.
Free tool
How ready is your business for AI?
Twenty questions, about four minutes. You get a score across five areas and what to fix first.