What an AI Consultancy Should Actually Deliver
An AI consultancy is worth hiring when it leaves you with working software and a team that understands it. What to expect at each stage, and what to be wary of.
“AI consultancy” can mean anything from a slide deck to a shipped product. Plenty of organisations have paid for an AI strategy that identified dozens of opportunities and delivered none of them. The gap between advice and working software is where most of the value is lost.
At Amygdal, an AI consultancy engagement means we help you find where AI creates real value, then design, build and integrate it into production. We are a software company that implements AI, so the strategy is grounded in what can actually be shipped. Here is what we think any AI consultancy should deliver, whoever you hire.
1. Opportunity discovery tied to decisions
A good AI consultancy starts by finding the opportunity, not the model. The useful question is where in your business AI would change a decision, save meaningful time, or enable a product you cannot offer today. That means interviews with the people doing the work, a look at the systems and data involved, and an honest assessment of value against effort.
The deliverable should be a short, ranked list of use cases, each with the decision it affects, the data it needs, the main risk, and how success would be measured. If the list is long and vague, the discovery was not finished.
2. A proof of concept on the riskiest assumption
Most AI projects die between a demo and a dependable product. The way to de-risk that is a focused proof of concept that tests the hardest assumption against real data. Can the system extract the right fields from your actual documents? Does retrieval find the correct policy clause? Is accuracy good enough in the cases that matter?
A proof of concept should produce evidence and a clear go or no-go recommendation in weeks. Be wary of one that produces a polished interface over a curated dataset — it proves the demo works, not the product.
3. Production software, not a handover document
This is where pure advisory firms step back and where the real work starts. Production AI is mostly software engineering: data pipelines, APIs, permissions, a usable interface, error handling, guardrails, monitoring and tests, integrated with the systems people already use. LLM and RAG features, computer vision, predictive analytics and recommendations all need the same treatment.
Our project history reflects that mix. On Sumus we built the AI tool behind a fashion product-discovery engine — recognition, ESG scoring and brand comparison. On Digital Arrow, an HR and recruiting platform, asynchronous video interviews with AI scoring sit inside a much larger product of timesheets, contracts and invoicing. In both cases the AI only works because the software around it does.
4. Evaluation and operations you can see
You should leave an engagement knowing how well the system works and how you will know if that changes. Concretely, an AI consultancy should hand over:
- An evaluation set of real examples with expected outcomes, and the results of running the system against it.
- A regression process that re-runs evaluation whenever prompts, models or data change.
- Monitoring for quality, latency, cost and usage, with alerts that someone owns.
- Guardrails appropriate to the risk: input validation, output checks, refusal behaviour and human review where needed.
- A record of decisions: which models, why, where data is processed, and what was tried and rejected.
5. Knowledge transfer, not dependency
A good partner accelerates you and leaves your team more capable. That means working in your repositories and infrastructure where possible, pairing with your engineers, documenting the system as it is built, and agreeing in advance how ownership transfers. If the engagement is structured so that only the consultancy can change the system, ask why.
What a typical engagement looks like
Engagements vary, but a sensible shape is short discovery, a time-boxed proof of concept, then a production phase with regular releases. Discovery and the proof of concept are usually measured in weeks and have a clear exit point: if the evidence is weak, you stop with a useful finding and a modest bill rather than a half-built system.
During the production phase, expect to see working software early and often — a release every week or two that real users can try — rather than a long build followed by a reveal. Expect the consultancy to raise risks plainly: data that is not good enough, an integration that will take longer than planned, a use case that is not delivering. Those conversations are uncomfortable, but they are the main reason to work with experienced people. A partner who only ever reports good news is not managing the project; they are managing you.
Finally, expect clarity on ownership from the first contract: your code, your prompts, your evaluation sets and your data, in your accounts.
When to hire an AI consultancy
Hire one when you know AI could help but do not have the in-house depth to choose the right approach, avoid expensive mistakes and ship it safely. That is especially true in regulated or high-stakes contexts such as healthcare and finance, and when AI will be part of a customer-facing product rather than an internal tool.
You probably do not need one if the need is covered by AI features in tools you already own, or if you have an experienced team that simply needs more hands — in that case, a development partner is a better fit than a consultancy.
Questions to ask a prospective AI partner
- Which AI systems have you put into production, and who maintains them now?
- How do you evaluate quality before and after launch?
- What would make you recommend we not build this?
- Where will our data be processed, and by which providers?
- How does ownership of code, prompts and evaluation sets transfer to us?
Talk to us
Amygdal works as an AI consultancy and development partner for clients in the UK, Germany, Switzerland and Qatar. Our custom AI development service takes you from discovery to production, and our guide to custom AI development vs off-the-shelf tools covers how to decide what to build. Get in touch to start with a discovery conversation.
