AI Integration Services for Fintech, Healthcare & Logistics that connect models to live workflows.
We provide AI integration services for teams adding model capabilities to a live product. We connect models to your software, data and workflows, then build the evaluation, deployment and operating arrangements around the feature.
- 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
AI features inside your existing product
We build the engineering connection between an AI capability and the product that will use it. Start with one workflow and a testable acceptance standard, then plan the data path, interface and rollout.
- Integration with your existing systemsProduction integration connects APIs, middleware or a microservice wrapper to the model. We plan the rollout around the modules and dependencies affected by the feature.
- An interface around the providerWe separate provider calls from product logic. A replacement model still needs compatibility checks, adaptation and evaluation against your acceptance criteria.
- Evaluations on real casesWe build evaluations and guardrails around representative cases, with a human-in-the-loop step where the workflow requires review. Your domain owner agrees the acceptance standard.
- 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
AI products and integrated search features
We bring experience from operating our own AI products and delivering search features inside a client's production platform.
- meinUnterricht measured the liftWe supported relevance, filtering and autosuggest alongside AI tagging and embeddings for meinUnterricht's search and discovery. The client reported a roughly 9% lift in search success in a controlled experiment across that feature work. The features were live in production, with the tagging and embeddings preparing the move to vector search.~9%higher search success rate, in a controlled experiment
- AI products we build and operateSnaplore turns meeting content into knowledge using Whisper, GPT models and AssemblyAI. Talkmetry is our call-intelligence product for HubSpot, using GPT and AssemblyAI. Both products are owned by unicrew and revenue-generating; Snaplore has active users.Up to 60%less time on documentation, reported by Snaplore's clients
- An internal workflow proof of conceptOur AI web agent used AWS Bedrock and LangChain to navigate unicrew's project management system, identify overdue time logs and notify the relevant people and managers through Google Chat. This was an internal proof of concept to test a browser-based administrative workflow.30%improvement in timely work-time logging compliance across unicrew's team, in an internal proof of concept
03Compare
Choose the integration route that fits
Compare your platform's native feature with an existing-model integration or a custom-model build. Your own engineers can implement an integration when they can cover the data, evaluation and operating work.
| Integrate a hosted modelunicrew | Train a custom modelBuild | Your platform's own AI featureBuy |
|---|---|---|
| Best forChat, summarization, classification, extraction or drafting where an available model meets the task's measured requirements. | Best forA task that needs model development after available models have been evaluated against quality and operating requirements. | Best forA workflow covered by the available features of Salesforce, ServiceNow, Dynamics 365, HubSpot or your SAP estate. |
| What it asks of youRepresentative cases, approved provider data terms, an operating budget and someone responsible for accepting the feature. | What it asks of youSuitable training data, model-development expertise, evaluation and a plan for hosting and maintaining the model. | What it asks of youChecking workflow fit, configuration options, permissions, data terms and subscription costs with the platform owner. |
| You end up owningThe integrated feature and agreed interface and evaluation deliverables, with provider services governed by their terms. | You end up owningThe commissioned model and deployment deliverables, with data and model rights defined by licences and the agreement. | You end up owningA configured workflow within the platform, with use and control governed by its subscription and feature terms. |
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 throughDefine the workflow and success measure
Identify the task, the data it needs and the result your domain owner would accept. That gives a scoping conversation a concrete starting point.
Talk it throughWorth scoping as an integration
Review the available models and native platform features against your workflow. A readiness assessment can establish the data work and first integration to investigate.
Book a discovery callIntegration is the right shape
For an existing product with a defined model task, scope the first system connection, data permissions and acceptance cases. These determine what the pilot needs to demonstrate.
Book a discovery callIntegrate, and start with a pilot
Test one workflow using representative data and agreed acceptance criteria. Use the result to decide the production integration's scope, operating responsibilities and next investment.
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
We build AI integrations around how far the model is allowed to reach into your systems, from answers in product screens to actions through connected tools. We scope specialist builds separately.
- Fixed-fee scoping
Where a model goes first
Keep, rebuild, or removeAI integration consulting starts with the readiness assessment when the first use case needs defining. In two weeks, for a fixed fee, we establish what to keep, rebuild or remove and what the first build costs.
- LLM integration services
LLM features inside your product
Behind your own interfaceWe add summarization, classification, extraction, drafting and chat to existing screens. Generative AI integration connects the model through a product interface, with provider choice assessed against quality, latency, data terms and cost.
- RAG development
Retrieval (RAG) over your own data
Answers with sources attachedWe build retrieval-augmented generation over your documents and records. The retrieval layer supplies relevant source material to the model; chunking, indexing, refresh and source citations become part of the integration and its evaluation.
- AI agent integration
AI agents inside your workflows
Multi-step tasksWe connect agents to the tools a multi-step task requires. Your requirements define permitted systems, actions and human review. The internal web-agent proof of concept explored this pattern in a browser-based administrative workflow.
- Permissioned tool access
MCP servers so models can use your tools
Auditable permissionsWe expose selected tools through a Model Context Protocol server and define the access controls the integration needs. MCP provides a tool interface; authorization and review requirements still need implementing in the connected systems.
- Speech-to-text and voice AI integration
Speech, transcription, and call intelligence
Whisper, GPT models, AssemblyAIWe connect transcription and call analysis to product workflows. For conversational AI and voice, the fixed-scope 30-day pilot tests the proposed use case on your recordings before a wider build.
- AI data engineering
The data work underneath
Tagging, embeddings, pipelinesWe prepare tagging, embeddings and data pipelines for the information the model needs. We assess source quality and refresh requirements alongside retrieval behavior and the product's acceptance cases.
- Ships with the integration
Evaluations and guardrails before it ships
Measured on your real casesWe build evaluations on representative cases, add input validation and guardrails, and define human-review points for consequential outputs. The suite is an integration deliverable; a standing evaluation and observability engagement belongs in AI QA and evals.
05Trust
What an AI integration hands over
We hand over the agreed deliverables so your team can operate the feature and assess future model changes. Provider changes may require adaptation and retesting; deliverable rights and third-party licences are defined in the agreement.
- 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
We choose model APIs, orchestration tools and cloud services for the integration's data path and operating requirements. REST APIs connect the feature to existing software; NLP supports language-processing tasks.
Python- REST API
OpenAI / ChatGPT- Whisper AI
AWS Bedrock
LangChain- NLP
Azure07Engagement
Scope, pilot or extend your team
Choose a readiness assessment, a scoped pilot or team extension according to the decision and implementation work ahead. There is no minimum engagement period.
Start with a readiness assessment
Fixed scopeWe assess which systems and data sources can support AI and prioritize the first use case. This is a fixed-fee, two-week assessment with its own written output, separate from a pilot or production build.
- 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 firstTest one use case on representative data and measure it against your acceptance cases. The pilot establishes a baseline for evaluating the production build and deciding what to implement next.
- 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-runExtend your engineering team for integration work across several workflows. Agree delivery and operating responsibilities with your technical lead. Continuing AI leadership is a separate mandate for an owner. Sourcing and vetting carry no separate charge under this team-extension model.
- 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
We review an inherited integration against representative cases, its data path and operating responsibilities. The assessment recommends what to keep, harden or replace. Commissioning changes or ongoing support is a separate decision.
Define the first integration
Bring the workflow and systems you want to connect. We will discuss whether the next step is a readiness assessment or a pilot with agreed acceptance criteria.
08Industries
Industries we integrate AI for
We scope integration around the sector's data and workflow requirements. Fintech, healthcare and logistics need different acceptance cases, access decisions and operational owners.
Logistics and transportation
We connect model features to order, warehouse and fleet workflows, with acceptance criteria for data freshness and task performance. Vehicle inventory and inspection software provides operational context for the field data those features need.
Hospitality and leisure
Our hotel platform integration work integrated Opera Cloud, updated IBS Software and Shift4 connections, and added booking middleware. That system experience, alongside entertainment booking software, informs the data connections an AI feature would need.
Fintech and accounting
We integrate language-processing features with bookkeeping and invoice workflows, agreeing review points for financial records. Our accounting automation work recorded backend information and matched it with invoices, with protected accounts and encrypted data.
Healthcare
We scope AI model connections for clinical workflows around data privacy and review requirements, drawing on our online medical-consultation platform work.
EdTech and learning
We connect search, tagging and embeddings to learning-content workflows. The edtech search project combined relevance, filtering and autosuggest with AI work, giving the feature a concrete search-success measure.
E-commerce
We connect product-information features to the systems that hold stock and orders. Our commerce integration work linked Shopify storefronts and Microsoft Dynamics through custom middleware, alongside an AWS-based product information management system.
Bring your workflow to an integration call
Tell us the task, the systems involved and what a useful result looks like. We will discuss model integration, native platform features and deterministic process automation against those requirements.
What happens after you contact us
- We reply within one business dayA response to your enquiry so we can establish the workflow and next step.
- 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 dateWe agree scope, access and the start date. Most engagements start within two to four weeks.
09Delivery
How an AI system integration runs
AI system integration moves from assessment and a measured pilot to production hardening and deployment. Your product and domain owners approve the acceptance criteria and operating responsibilities.
- AI readiness assessmentThe fixed-fee readiness assessment takes two weeks to establish what to keep, rebuild or remove and what the first build costs. For this diagnostic, we establish an NDA and agree read-only, least-privilege access with your technical contact before starting.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 dataWe test one workflow against representative cases and measure the result. You provide the data and a domain owner who can judge task quality and business value; we agree the acceptance standard for the production build.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. Your security and domain owners define the applicable data requirements and the steps that need human review, with legal input where required.You getAn evaluation suite on your real cases, guardrails and input validation, cost controls, and a documented human-review threshold.
- Integrate and monitorWe deploy inside your stack and configure the agreed monitoring for quality, cost and drift. You provide target-environment access and an operating owner; we define how findings are reviewed and changes approved as data and usage evolve.You getThe integration live in your stack, monitoring for quality, cost and drift, and an agreed cadence for reviewing results and deciding adjustments.
10Client voices
Client perspectives on integration work
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
unicrew was formerly Artelogic, so the older reviews use that name. Our experience includes an owned production product, client search features and an internal workflow proof of concept.
See all case studies
Built by unicrewAI-powered knowledge managementSnaplore 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 time-consuming internal administrative task.30%Improvement in timely work-time logging compliance
12Questions
About our AI Integration Services
An AI integration needs clear provider, data and operating arrangements. Agree acceptance, handover and responsibility for future changes alongside the feature's initial scope.
We quote the build against its scope and estimate running costs separately. The main drivers are:
- Data preparation and the systems connected.
- Evaluation depth, review requirements and integration complexity.
- Model usage, hosting and ongoing operating work.
For a workflow based on stable, explicit rules, consider process automation alongside AI. The AI readiness assessment is fixed fee; other engagements follow the agreed model:
- Time and materials: billed hourly, quoted per project.
- Fixed price: outcome based, quoted per project.
- Team extension: billed monthly per team member.
We estimate the AI system integration timeline after assessing the workflow, data readiness, system connections and acceptance requirements. Pilot and production work are scoped separately.
The fixed-fee AI readiness assessment takes two weeks at its defined scope. That duration does not set the pilot or deployment schedule. Most engagements start within two to four weeks; we agree the actual start and delivery plan for your project.
Yes, where the system's interfaces, data access and operating constraints support the proposed workflow. We assess those integration points before planning the build.
APIs, middleware or microservice wrappers may provide a connection; a database or scheduled export can be an option where permitted. Bookkeeping and invoice automation illustrates the existing workflows an integration must account for. If the application itself needs substantial change, we scope legacy modernization separately.
We select AI technologies for the task and the system they need to connect to. Candidate approaches include:
- LLM integration through model APIs or an open-weight deployment for chat, extraction and drafting.
- Retrieval-augmented generation, NLP and OCR for document and knowledge workflows.
- Speech recognition or predictive models where those capabilities fit the data and acceptance requirements.
OpenAI, Anthropic, AWS Bedrock and Azure OpenAI are provider options; GPT, Claude, Llama and Mistral are model families to evaluate. Selection follows testing and the permitted data path.
We consider hosted APIs, open-weight models and combinations of the two. The choice depends on task quality, latency, data terms, usage cost and the infrastructure your team can operate.
Self-hosting gives you responsibility for deployment, compute, access and maintenance; it does not require training a model from scratch. Hosted services have their own data and lifecycle terms. A hybrid can separate workflows with different requirements, provided the integration and permissions support that design.
Custom AI integration connects an existing model or AI service to your product, data and workflow. Model development changes or trains the model itself.
Generative AI integration may use retrieval and prompting without model training. Fine-tuning or a task-specific model becomes a separate AI and ML development question when testing shows that available approaches do not meet the requirements. Self-hosting an existing model is a deployment choice, not the same as training one.
We define the integration's data path, access and review requirements with your security, domain and legal owners.
- Agree which data reaches the model, what is excluded and who can access it.
- Review provider terms, logging and retention requirements for the selected service.
- Define evaluation records and human oversight appropriate to the use case.
Your legal owner determines which GDPR, HIPAA, PCI-DSS and EU AI Act requirements apply to the use case and your role in developing or using the system. unicrew holds ISO 27001:2022 and ISO 9001:2015 certifications, renewed through a multi-stage audit with Quay Audit UK. Those are organizational credentials.
Ownership, licences and handover are defined in the agreement, together with applicable data and model-provider terms. Agree those rights before implementation.
The handover scope can include the interface, evaluation suite and baseline, prompts, retrieval index and data-path documentation. Your data rights, third-party licences and the provider's output, retention and training terms remain relevant. We identify those dependencies so the operating team knows which artifacts it receives and which services it still needs.
We evaluate the feature against representative cases and agree how its quality will be reviewed in operation.
Retrieval can ground answers in relevant sources; evaluations, input validation and guardrails help identify unsuitable behavior. Human review remains important for consequential outputs, and grounding does not eliminate hallucinations. Monitoring covers the agreed quality, cost and drift signals, with an owner and review cadence for deciding adjustments as the data or usage changes.
We plan a replacement and retest the integration; a retired model endpoint can stop accepting requests. The provider interface reduces coupling, but a migration may still require changes to API calls, prompts, tool behavior or deployment.
Evaluate the replacement on the acceptance cases, including quality, latency and cost, before approving rollout. Responsibility for watching provider changes and carrying out the migration belongs in the operating agreement. A provider switch is an engineering change with compatibility and retesting work.
Enforced tool permissions and approval rules constrain the actions available to the agent. Instructions in a prompt alone are not an access-control boundary.
We build agents to client requirements, including the systems and actions permitted by the specification. Human-review points need implementing for the actions that require approval, and the connected applications must enforce their authorization rules. MCP supplies an interface to tools; it does not automatically grant or police those permissions.
We offer AI integration for fintech, healthcare and logistics and transportation, alongside education and e-commerce.
Scope follows the workflow: financial records need appropriate review and access; clinical applications need domain and data approval; operational systems need acceptance cases for freshness and task performance. Our software experience in those sectors informs the integration, while the search and owned-product work provides the specific AI proof.
The AI readiness assessment is a fixed-fee, two-week review of the systems and data in scope and what the first build costs.
It produces:
- A keep, rebuild or remove map.
- A use-case shortlist ranked by return against delivery risk.
- A costed plan for the first build.
We agree the scope and fee in writing before starting. The assessment informs a subsequent implementation decision; its duration applies to the assessment itself.
We assess the platform's native features, APIs and permissions before designing a custom connection. Connectors, webhooks or middleware can bridge the platform and model where its interfaces and terms support the workflow.
Storefront-to-ERP data, booking-system connections and CRM records each have different integration boundaries. We define which business logic stays in the platform, which data reaches the AI service and what the operating team must maintain. A native feature may already meet the requirement; custom integration extends the workflow where needed.
Compare relevant delivery scope, acceptance evidence and responsibility for operating the feature.
- Ask what the pilot measures and who approves the result. Our pilot establishes a baseline on representative cases.
- Distinguish client production work, owned products and internal proofs of concept when assessing examples.
- Agree the implementation team, handover and owner of provider, data and quality changes.
- Check organizational credentials and access arrangements against your requirements.
- Read the client evidence behind the rating. Our unified Clutch display is 5.0 across 61 verified reviews.
Our guide expands on evaluating AI integration vendors.