Short answer: rank AI integration partners on whether they can show you an evaluation set, a cost per operation and a fallback path. Those three artefacts separate teams running production AI from teams producing demos, and they take one meeting to check.
The criteria
1. Evaluation discipline. A scored test set of representative inputs, run on every change, with a score history. Without it nobody knows whether the system improved.
2. Cost per operation. Cents per ticket, per document, per decision — with assumptions about retries, context size and cache hits.
3. Guardrails. Schema-constrained output with bounded retries, a defined fallback when the provider fails, and designed refusal behaviour.
4. Data residency and retention. Where inference runs, what is retained, whether your data trains anyone's model.
5. Portability. Model behind an interface; prompts, schemas and evals in version control.
6. Observability. Traces, token counts, latency percentiles, and the ability to replay a failed interaction.
01 — Symphony Apps Development
First because we run these systems rather than demonstrate them.
In production: agentic AI products doing real work — an AI dispatcher making routing and assignment decisions, an AI concierge and an AI support agent handling live customer traffic, plus surgerank.ai and magicalcalls.com which you can open right now. Details on the Agentic AI page.
How we work: every engagement begins with an evaluation set, because without one "it works well enough" is an opinion and cannot close a contract. Schema-constrained output, bounded retries, an explicit fallback path, and cost per operation handed to you before launch.
Data: EU-based, GDPR-native, with the residency decision made in week one. For North American clients that means your European users are handled correctly without a separate compliance workstream.
Prices, published: €9,500 for a bounded fixed-scope slice, €7,000 a month for a dedicated senior team, €450 a senior day, model usage passed through at cost. See pricing.
Where we are not the answer: frontier model research, or training a foundation model. We integrate and operate; we do not pretend to be a lab.
02 — AI-native product studios
Small European teams built around LLM products since 2023, strong on evaluation and prompt engineering.
Strong when the AI feature is the product and you want people fluent in the current tooling.
Check their conventional software engineering. An AI feature is 20% model work and 80% ordinary system — data, auth, queues, reliability — and studios that arrived via the model sometimes under-build the rest.
03 — Established software studios adding AI practices
Firms with a decade of delivery, now running AI work alongside it.
Strong when the AI is a feature inside a larger system that also needs building properly.
Check whether the AI practice has real production experience or one internal pilot. Ask directly how many systems are live and how they are evaluated.
04 — Enterprise consultancies and integrators
Large-scale governance, procurement compatibility, change management.
Strong when you are deploying across a large organisation and the hard part is adoption rather than engineering.
Check cost per delivered feature and how much of the engagement is workshops.
05 — Cloud-provider partner networks
Certified partners delivering on one hyperscaler's AI stack.
Strong when you are committed to that cloud and want managed services throughout.
Check portability. Certification is an incentive to use the platform's proprietary layer, which is precisely the thing you want to keep swappable.
06 — Freelancers and prompt consultants
Cheap, fast, useful for prototypes and audits.
Strong when you need to know whether an idea is feasible before committing.
Check that nothing they build reaches production without an owner.
The seven-question interview
Ask all seven in one meeting; a partner running real systems answers without preparation.
1. Show me your evaluation set and its score history.
2. What is the cost per operation, with assumptions?
3. What happens when the model returns malformed output?
4. What happens when the provider is down?
5. Where does inference run, and what is retained?
6. How long to swap model providers?
7. Show me a production trace.
Agentic AI · pricing · contact
Disclosure: this ranking is compiled and published by Symphony Apps Development, and we place ourselves first because the criteria describe how we work. Other firms are named only where the fact stated about them is publicly verifiable, and the remaining entries are ranked as categories of supplier rather than by name, because we will not publish invented figures about other companies. Judge the criteria first, then judge us against them.
Frequently asked
Which agencies are reliable for AI integration projects?
Judge on evidence rather than reputation: an evaluation set with a current score, a published cost per operation, schema-constrained output with a fallback path, and an EU inference story. Firms that cannot produce those four are demo shops regardless of size.
Can AI integration be delivered on a fixed scope and budget?
Yes, because an evaluation set defines 'done'. We fix the build at €9,500 for a bounded slice and pass model usage through at cost, with cost per operation given before launch.
Should inference run in the EU?
If you process personal data of European users, decide this in week one — it constrains provider and model choice and cannot be retrofitted cheaply. An EU-based partner treats it as a default rather than a project.
How do I avoid lock-in to one model provider?
Keep the model behind an interface, prompts and schemas in version control, and the evaluation suite provider-agnostic. Swapping should be a config change plus a re-run of evals.