AI Integration Services for Fintech, Healthcare & Logistics that wire models into systems carrying users.
AI integration services connect a model to the software, data, and workflows you already run, so the feature ships inside a live product rather than stopping at a pilot.
- 120+Projects delivered across 12 countries since 2012
- 100+Senior in-house engineers, six countries
- 5.0Unified rating across 61 client reviews on Clutch
- ISO 27001Certified security practice, audited by Quay Audit UK
- AWS CertifiedSolutions Architects on the team
- 5Offices: Ukraine, Poland, Estonia, the UK, and the US
01Overview
Why do AI pilots stall before production?
Roughly 95% of enterprise generative-AI pilots deliver no measurable return (MIT, State of AI in Business 2025, reported by Forbes). The model is rarely what failed. A demo and a deployed application are different things, and the distance between them is engineering work. The engineer we embedded on meinUnterricht's search and discovery team shipped features that are live in production, and a controlled experiment measured a roughly 9% lift in search success. unicrew does that half, against a bottleneck you can already name.
- Your systems keep runningAPIs, middleware, or a microservice wrapper around the model, added one module at a time, so nothing goes dark during the rollout.
- The provider stays swappableThe model sits behind your own interface, so switching vendors, or routing between them on quality, speed and cost, is configuration rather than a rewrite.
- Evaluated before a user sees itAn evaluation suite on your real cases, guardrails, and a human-in-the-loop step agreed while the integration is still cheap to change.
- The rest of the AI clusterAutonomous agents when the job is multi-step, custom models when no hosted one fits, and an MCP server when the model needs your tools.
02Proof
Why clients choose us for AI integration
unicrew runs AI inside its own products and against its own team, so retrieval that drifts, token cost at real volume and an agent nobody owns land on us first.
- meinUnterricht measured the liftOur engineer sat inside their search and discovery team, and the AI tagging and embeddings built there set up their move to vector search. Those features run in production.~9%higher search success rate, in a controlled experiment
- We ship AI to production, not to a demoWe build and run our own AI products: Snaplore for meeting knowledge and Talkmetry for call intelligence. Both are revenue-generating.Up to 60%less time on documentation, reported by Snaplore's clients
- An agent we pointed at our own teamAn AI web agent on AWS Bedrock and LangChain that works a repetitive browser-based admin task across the team. Internal proof of concept, 2026.30%improvement in timely work-time logging compliance across the team, from an agent we run on ourselves
03Compare
Integrate, build your own, or use what you already pay for?
Three buys, and the first check is on the third: what the platform subscription you already pay for ships today, so the integration covers what it does not. Retrieval (RAG) over your documents and fine-tuning a hosted model are not a fourth and a fifth option; both are decisions taken inside an integration. Retrieval keeps the source of truth in your documents and moves when they do; fine-tuning buys a cheaper, faster model on a narrow task and charges you a retraining cycle every time the task changes.
| Integrate a hosted modelunicrew | Train a custom modelBuild | Your platform's own AI featureBuy |
|---|---|---|
| Best forChat, summarization, classification, extraction, and drafting inside a product you already run, where a general model is good enough. | Best forProprietary data, strict privacy or latency limits, or a task no general model handles. | Best forA standard task inside a platform you already pay for: Salesforce, ServiceNow, Dynamics 365, HubSpot, or your SAP estate. |
| What it asks of youA per-token budget that scales with usage, and data terms agreed with the model provider behind your interface. | What it asks of youThe biggest budget and the longest timeline of the three, plus training, hosting, and maintenance for as long as it runs. | What it asks of youAccepting the platform's roadmap in place of yours, and its data path as it stands. |
| You end up owningThe feature, the interface the provider sits behind, and the evaluation suite that scores it. | You end up owningThe model, its training data, and the infrastructure it runs on. | You end up owningA subscription, and whatever the vendor ships next. |
Quick self-check
Tick what is true for you. The read-out updates as you go.
0 of 4 true
Start with the AI build that fits
A neighbouring build matches these answers. A model trained on your own data is AI and ML development; software that takes a goal and runs the steps itself is an AI agent. Still deciding where AI pays off? Start with the readiness assessment.
Talk it throughOne signal, worth thirty minutes
A single signal usually starts as a data question. We answer that first, with a query and a report where they do the job, and bring the model in where they stop.
Talk it throughWorth scoping as an integration
Two signals usually means the integration is real but the first use case is not settled. That is what the readiness assessment is for: a costed plan for the first build, not a commitment to the rest.
Book a discovery callIntegration is the right shape
Three signals is the profile most of our integrations start from. What is left to settle is which system the model reaches first, and where that data is allowed to go.
Book a discovery callIntegrate, and start with a pilot
All four. This is the case that goes wrong bought as a standalone tool beside the product, because the value is in the systems it reaches rather than in the model. Start on one workflow, not on the roadmap.
Talk about the workWe pilot an AI integration on your real data and on one workflow you already run. The pilot ends in a measured accuracy baseline, and the production build is held to that same bar.
Tural MamedovChief Executive Officer, unicrew04Capabilities
What we integrate
Integration work divides by how far into your systems the model is allowed to reach: one screen, your own documents, your tools, or the work itself. Where a card is a bigger job than an integration, it links to the page that owns it.
- Fixed-fee scoping
Where a model goes first
Keep, rebuild, or removeAI integration consulting in its shortest useful form: a fixed fee, two weeks, spent deciding which candidate use case is worth building first and whether your data can carry it yet.
- LLM integration services
LLM features inside your product
Behind your own interfaceSummarization, classification, extraction, drafting, and chat wired into screens your users already use, with the provider behind your own interface so swapping it is a config change.
- RAG development
Retrieval (RAG) over your own data
Answers with sources attachedA retrieval layer that answers from your documents with sources attached, including the chunking, indexing, and refresh strategy that decides whether it is still right six months later.
- AI agent integration
AI agents that do work, not just answer
Multi-step tasksAgents that navigate your tools and complete multi-step tasks, built to your requirements: what the agent may touch, and which of its actions wait for a human decision, are part of the specification you set. Our internal web agent chases delinquent time logs across the team.
- Permissioned tool access
MCP servers so models can use your tools
Auditable permissionsA Model Context Protocol server exposes your systems to a model under explicit, auditable permissions, rather than credentials in prompts or a connector per tool.
- Speech-to-text and voice AI integration
Speech, transcription, and call intelligence
Whisper, GPT models, AssemblyAIMeetings and calls turned into searchable, structured knowledge. It is what our own Snaplore and Talkmetry do in production, and wiring the same pattern into your product is conversational AI and voice work. The fixed-scope 30-day pilot proves it on your own recordings first.
- AI data engineering
The data work underneath
Tagging, embeddings, pipelinesTagging, embeddings, and pipelines are usually the real project, and retrieval quality is decided there rather than in the prompt.
- Ships with the integration
Evaluations and guardrails before it ships
Measured on your real casesAn evaluation suite on your real cases, plus input validation, guardrails, and a defined human-review threshold, so a bad response cannot quietly break the workflow around it. That suite ships with the integration. Buying evaluation and observability as a standing engagement is AI QA and evals.
05Trust
What an AI integration hands over
What you keep is not the model, which you could rent yourself. It is the interface the provider sits behind, the evaluation suite that says whether the answers are good enough, and a written record of where your data went. All three survive a change of vendor.
- SwappableThe provider behind your own interfaceOpenAI, Anthropic, or an open-weight model inside your own environment, chosen per feature rather than once per company
- Yours to keepAn evaluation suite on your real casesIt runs without us, which is what makes a change of model or of vendor a decision rather than a leap of faith
- AuditableThe data path written downWhat leaves your environment, what is logged, and who can see it, written down rather than assumed
06Stack
Technologies we integrate with
What we have shipped on, not a menu. Two entries are not models: REST API is how a model reaches your systems, and NLP is the older technique still doing classification and extraction underneath. AWS and Azure are here because the deployment target is usually yours.
Python- REST API
OpenAI / ChatGPT- Whisper AI
AWS Bedrock
LangChain- NLP
Azure07Engagement
How you engage us, and how you pay
Three ways in, and most clients start with the assessment or a pilot. There is no minimum engagement period, so each next step is a decision you take on evidence, and we recommend which of the three fits before you commit.
Start with a readiness assessment
Most askedA fixed-scope look at where AI fits and what to build first, so the budget conversation happens after the evidence rather than before it. The assessment is a complete deliverable in its own right.
- Best when
- You know AI should help somewhere and want the where before the budget
- You pay
- Outcome based, quoted per project
- Typical start
- Two to four weeks
Run a scoped pilot
Evidence firstProve one high-value use case on your real data, then decide whether to scale it. It ends in a measured accuracy baseline, which is the bar the full build is held to.
- Best when
- You have one use case and need evidence before the full build
- You pay
- Billed hourly, quoted per project
- Typical start
- Two to four weeks
Add a dedicated AI team
Long-runA senior squad that integrates AI across your product and stays with it as models change underneath. Drift and token cost need an owner, and this is the shape that gives them one. Nothing separate is charged for sourcing, vetting or placing an engineer.
- Best when
- You have more than one workflow to integrate and want one team accountable across them
- You pay
- Billed monthly, per team member
- Typical start
- Two to four weeks
An assessment first, and the rebuild is a separate decision
The integrations we get asked to look at usually stall for one of two reasons: no evaluation suite, so nobody can say whether the answers are good enough, and no owner, so quality drifts as the models and your data move. A takeover starts by scoring what exists against your real cases. You get a written assessment with a keep, harden, or replace recommendation, and handing us the build or the ongoing support afterwards is a separate decision.
Not sure where AI fits yet?
Start with a scoping call. We will map where AI pays off in your stack and size the first use case, with no commitment to a full build.
08Industries
Industries we integrate AI for
We integrate AI where the data is regulated or operational, so the guardrails matter as much as the model. Fintech and healthcare are buying auditability: who saw which data, what the model produced, who signed it off. Logistics is buying throughput. These are the sectors we have shipped software and AI into, so we ramp on your rules rather than on your budget.
Logistics and transportation
Operations software that cannot stop while a model is added to it: order management, warehouse, and fleet tooling. One logistics and transportation provider we have built for has managed inventory and inspections on well over 9 million vehicles. Here a model earns its place on throughput and freshness.
Hospitality and leisure
On one hotel platform we integrated Opera Cloud, updated the IBS Software and Shift4 connections, and built a booking engine that works as middleware to third-party systems. We also built a platform for the entertainment industry. That reach across systems is what an integration depends on.
Fintech and accounting
We turned an unfinished ASP.NET MVC and C# build into the NLP-assisted accounting automation a UK financial advisory firm reconciles transactions on.
Healthcare
The constraint is the environment before the model: a platform we built for online medical consultations runs HIPAA-ready, with databases in a private subnet that is inaccessible from the internet, and developers holding no access to those databases.
EdTech and learning
A teacher searching a curated library will not scroll past the first few results, so a plausible wrong answer costs them the preparation time they came to save. That is what the retrieval work under one German edtech platform's search is for.
E-commerce
One European manufacturer sells in seven EU countries, B2B and B2C, and manufactured more than 10,000 products. Shopify storefronts joined to a Microsoft Dynamics ERP through custom middleware is the shape of that work, and it is the shape a retrieval layer has to keep up with.
Name the workflow, and we will show you whether a model belongs in it
Thirty minutes with an engineer who ships integrations, not a qualifier call. You leave knowing whether a model belongs in the workflow, which part a rules engine you can read handles better, and what the platform you pay for already covers.
What happens after you contact us
- We reply within one business dayAn engineer reads what you sent, not an autoresponder.
- A call about the workflow and the systems it touchesWhat the manual work is, which systems the model would reach, and whether the platform you already pay for ships the feature.
- A written scope for the first integrationThe use case we would build first, the data and access it needs, and whether it starts as an assessment or a pilot on your real data.
- Contracts and NDAs, then a start dateSigned before anyone touches your systems or your data. Most engagements start within two to four weeks.
09Delivery
How an AI system integration runs
Four stages: an assessment, a pilot on your real data, production hardening, then the integration live and monitored. Stage two decides everything after it, because the accuracy baseline it measures is what the full build is held to.
- AI readiness assessmentTwo weeks, fixed fee. We map your workflows to find where AI pays off and size the first use case, backed by an audit of the data, APIs, access, and security involved. You provide read access to the code, data, and documentation in scope.You getA keep, rebuild, or remove map, a use-case shortlist ranked by return and delivery risk, and a costed plan for the first build.
- Scoped pilot on real dataTypically 2 to 4 weeks for a single feature on one AI API, in our delivery experience. You judge accuracy and value on your own data rather than on a demo, and you provide representative cases and one person who can rule whether an answer is right.You getA working pilot on your own data, plus a measured accuracy baseline you can hold the full build to.
- Production hardeningWe add authentication, error handling, evaluations, guardrails, and cost controls so the integration is safe to run rather than safe to demonstrate. You provide your regulatory constraints (GDPR, HIPAA, PCI-DSS) and a decision on which steps keep a human in the loop.You getAn evaluation suite on your real cases, guardrails and input validation, cost controls, and a documented human-review threshold.
- Integrate and monitorA single-module integration typically runs 4 to 8 weeks, a legacy-system one with architectural changes 12 to 20 weeks, in our delivery experience. We deploy inside your stack and track quality, cost, and drift as your data and usage change. You provide access to the target environment and the person who owns the integration once it is live.You getThe integration live in your stack, monitoring for quality, cost, and drift, and a retuning cadence so slipping accuracy is caught by us, not by your users.
10Client voices
Our clients say
Our headline result was a controlled experiment showing a roughly 9 percent lift in search success rate. The features built are live in production, and the AI tagging and embeddings work set up our move to vector search. What stands out most is their ability to own work end to end, from user-facing search features to the AI and data layer underneath.
Two people typing data into the database went about 5,000 new data sets per month. With this component, we’re reaching twice the amount of data sets per month. Their knowledge of different technologies is astounding. We can raise any technical issue with them and find someone within their team to work with that specific technology.
The module was integrated in our systems and almost doesn’t requires any maintenance till now. Project was managed in Jira in very effective way. The most impressive part of this company is people. It was a great pleasure to communicate with every person in this company.
We’re one of unicrew’s smaller clients, but they’re always incredibly quick to respond. When we have issues, they treat them as if they’re extremely important. We feel like we have a partner, and that’s been incredible. That’s why we’ll keep them on for as long as we can.
Even though Artelogic didn’t have a background in this area, they learned quickly and repurposed technologies they’d used before in order to solve the business problem. I was very impressed with this ability, as most of the people we contacted before implied that they’d need to spend a lot of time of trying to understand our business logic.
11Case studies
AI integration in production
Two of the three below are ours: our own product, and an agent running on our own team. unicrew was formerly Artelogic, so the older reviews use that name.
See all case studies
Built by unicrewRevolutionizing Knowledge Management powered with AISnaplore is unicrew's own product, built and operated in-house. It uses AI to transform how organizations document, structure, and share information, making meetings, training, and project discussions instantly accessible and actionable.Up to 60%Less time on documentation
Built by unicrewAI-Powered Automation for Project ManagementAn AI bot that enhances operational efficiency by automating a critical, time-consuming, internal administrative task.30%Improvement in timely work-time logging compliance
12Questions
About our AI Integration Services
An integration raises questions a greenfield build never does: what happens when the provider retires the model you built on, who owns the prompts and the outputs when it is over, and whether the platform you are built on ships the feature itself next quarter.
We scope before we quote, because the same feature is a two-week job or a two-quarter one depending on the state of your data. Four things drive it:
- how clean that data is
- how many systems the integration touches
- how much accuracy the task needs (evaluation work grows fast when a wrong answer is expensive)
- the per-token or hosting cost of running the model, which is an operating line rather than a build line
The return side is easier to size, and worth sizing before you brief anyone: take the manual step you want removed, multiply the minutes by the volume, and hold that against what inference and hosting cost at the same volume. If the two land within a factor of two, the return sits in the process underneath, and process automation is the stronger first build.
Past the fixed-fee AI readiness assessment, engagements run on time and materials, fixed price, or team extension, and the estimate comes out of scoping.
Scope determines the timeline. As a general guide, in our delivery experience: a proof of concept covering a single feature with one AI API takes 2 to 4 weeks, a single-module integration such as a document-processing pipeline or LLM-powered search takes 4 to 8 weeks, a multi-module or system-wide AI integration takes 8 to 16 weeks, and a legacy-system AI integration with architectural changes takes 12 to 20 weeks. The AI readiness assessment that sizes the first use case is two weeks, fixed fee.
Yes. Embedding AI into existing systems is one of our most common engagements. We audit the existing architecture for safe integration points, then ship incrementally through APIs, middleware, or microservice wrappers rather than rebuilding, so the system keeps operating during the rollout. Typical shapes are document processing via OCR and NLP, predictive analytics over existing pipelines, LLM-powered search, and NLP-assisted transaction reconciliation inside a bookkeeping system we inherited half-built. Where no API exists at all, the integration point is usually the database or a scheduled export rather than the application. More than that is legacy modernization.
Large language models such as GPT-4, Claude, Llama, and Mistral for document processing, chat, and knowledge retrieval; the APIs they arrive through, including OpenAI, Anthropic, AWS Bedrock, and Azure OpenAI; NLP for entity extraction, classification, and summarization; computer vision and OCR; retrieval-augmented generation over your own data; and predictive models for forecasting and anomaly detection. The strip above is what we have shipped on, not a menu.
Both, and what decides it is where the data is allowed to go rather than which model is better. A hosted API from OpenAI, Anthropic, or Azure OpenAI is the shortest path and where most integrations start. An open-weight model such as Llama or Mistral, self-hosted in your own cloud or on your own hardware, is the answer when data cannot leave your environment, when a per-token bill is the wrong shape for your volume, or when you need a version nobody else can retire. Hybrids are common: the sensitive path stays inside, the rest goes to the API. Our own Snaplore and Talkmetry run on hosted models, so it is a trade-off we make with our own money too.
Integration connects existing AI services, APIs, or pre-trained models into your product, which is faster and cheaper. Training a model on your proprietary data is the right call when no available model meets your accuracy, latency, or privacy requirements, and that is AI and ML development rather than this page. Our AI work starts from proven foundation models and takes them into production: we integrate, fine-tune, and build task-specific models.
Security is built into the integration rather than bolted on afterwards. unicrew is ISO 27001:2022 and ISO 9001:2015 certified, renewed through a multi-stage audit with Quay Audit UK. On access, we work under least-privilege access agreed with your technical contact, read-only wherever the work allows, and no write access to production systems during discovery. Where data cannot leave your environment, the answer is a private or self-hosted model rather than a hosted API. Where GDPR, HIPAA, or PCI-DSS applies, the constraint is yours to state and it shapes the scope: which fields reach a prompt at all, what is masked or excluded, and what the retention record has to show. One date is already behind you rather than ahead. The EU AI Act has applied since 2 August 2026, with the Article 6(1) high-risk obligations following on 2 August 2027, so an integration starting now is being built under the first and ahead of the second. Whether your use case is in scope is your counsel's call; what it changes for us is how much of the data path, the review step and the evaluation record gets written down as we go.
Your contract settles it, and it is signed before anyone touches your systems or your data, so the ownership clauses are in the agreement you read before anyone starts. Separate from the contract is what physically survives, because that is what decides whether you are free to leave: the interface the provider sits behind, the evaluation suite and the accuracy baseline it measured, the prompts, the retrieval index, and the written record of the data path. None of those belong to the model vendor. The one layer the model provider sets is its own retention and training terms, which differ by tier and by whether the agreement is a consumer or an enterprise one, so we settle which tier your data crosses at scoping rather than leaving it for an audit finding later.
We treat reliability as an engineering problem, not a hope. Before launch we build evaluations that measure accuracy on your real cases, and add guardrails, input validation, and error handling so a bad response cannot break the workflow around it. A hallucination is rarely fixed by a better prompt. It is fixed by giving the model the right documents to answer from, scoring what comes back against cases where you already know the answer, and putting a person in front of the steps where being wrong is expensive. In production we monitor quality, cost, and drift, so a slip is retuned rather than reported by a user. Higher-risk steps keep a documented human-review threshold, and that threshold moves only once the system has earned it.
You own the interface the provider sits behind, so moving to a different model is a configuration change rather than a rewrite of the feature around it. What makes that move decidable is the evaluation suite and the accuracy baseline it measured: a replacement is scored on the same cases before it reaches a user, so if it is worse you know that before your users do. Neither the suite nor the baseline belongs to the provider, which is the point of building it that way.
The specification. We build agents for clients according to the client's requirements, so what an agent may do is set by your specification rather than by a product default. Which systems it may touch, and which of its actions wait for a human decision, are yours to set at scoping. An agent reaches your tools through explicit, auditable permissions rather than credentials in prompts, and production hardening ends with a documented human-review threshold naming which actions wait for a person.
Mostly regulated and operations-heavy ones: fintech, healthcare, and logistics and transportation, alongside education and e-commerce. The pattern is similar across them; the constraints are not, which is compliance and auditability in fintech and healthcare, throughput and freshness in logistics.
Our AI readiness assessment is a structured evaluation of whether your product and data are ready for AI. It covers a stack review for integration points and blockers, a data quality audit, use-case prioritization ranked by expected return and delivery risk, compliance mapping for constraints such as GDPR, HIPAA, and PCI-DSS, and a cost and timeline estimate for the top-priority integration.
We integrate at the application layer, building connectors, webhooks, or middleware that bridge your platform and the AI service without replacing the infrastructure underneath. Platforms we have integrated: Shopify storefronts joined to a Microsoft Dynamics ERP through a custom middleware app, and Opera Cloud plus updated IBS Software and Shift4 connections on a hotel booking platform. On the CRM side we integrate Salesforce and HubSpot with custom software, two-way data flows and a clear line on which business logic stays out of the CRM, and our own Talkmetry is a call-intelligence product built for HubSpot. AWS and Azure are usually the deployment target rather than the integration itself. Where the platform ships its own AI feature we evaluate that first and build around it, so the integration covers what the platform does not.
Compare a level below the headline, on five things you can check. First, what the pilot is measured against and who rules whether an answer is right, because a pilot with no baseline is a demo with a longer runtime: ours ends in a measured accuracy baseline on your own cases, ruled on by one person on your side. Second, integrations from the last twelve months still in daily production, rather than ones that demonstrated well. Third, who does the work, because whoever tuned the prompts should still be reachable in month six: unicrew has 100+ senior in-house engineers across six countries, and no project is passed down a subcontractor chain. Fourth, the certifications and who audited them: ours are ISO 27001:2022 and ISO 9001:2015, audited by Quay Audit UK. Fifth, the reviews behind the rating: ours is Clutch's own unified rating, 5.0 across 61 verified client reviews, and every quote on this page links to the review it came from. The long version is our own guide to evaluating AI integration vendors.