Utvecklingsföretag för AI-copilot

We build AI copilots that integrate into your systems and support your teams across knowledge retrieval, content creation, and process automation. Instead of wasting time, you focus on the decisions that move the business forward.

30+

AI copilots built

40+

AI-ingenjörer

93%

återkommande kunder

We build AI copilots that integrate into your systems and support your teams across knowledge retrieval, content creation, and process automation. Instead of wasting time, you focus on the decisions that move the business forward.

30+

AI copilots built

40+

AI-ingenjörer

93%

återkommande kunder

Key benefits of AI copilot development solutions

AI copilots change how your organization finds and acts on information by removing barriers between asking a question and getting a reliable answer. The metrics below reflect results our clients see across real enterprise deployments.

Custom AI copilot development services

  • Consulting & strategy
  • MVP & PoC
  • Utveckling
  • Utbildning av modeller
  • Modernisering
  • RAG development
  • Enterprise search
  • Copilot integration

AI copilot consulting and strategy

We perform analysis of your corporate workflow and document landscapes, and identify where to use an AI copilot. We then define the system architecture and integration path that will best suit your architecture.

IT consultants conducting a project kickoff meeting in a modern glass office

MVP and proof of concept development

We build prototypes to provide proof of practical feasibility and validate answer quality using actual data from your organization, allowing your stakeholders to assess the real AI copilot performance before initiating our full-cycle AI copilot development services.

Writing and reviewing source code in a modern programming environment

End-to-end AI copilot development

We manage the entire lifecycle from capturing requirements and designing secure system architecture to selecting models, building the RAG pipeline, testing the solution, and deploying an AI copilot in your production environment.

Tech specialist focused on coding in an open-plan office environment with digital workstations

AI model training and fine-tuning

We train and fine-tune large language models using your proprietary internal data to provide higher relevance of answers and greater accuracy against the specific terminology, processes, and domain knowledge relevant to your business.

Software engineer integrating ML-driven virtual avatars into enterprise systems for advanced user interaction

Legacy system AI modernization

We integrate AI copilot capabilities into your existing corporate software to replace outdated text search with conversational access to your internal knowledge without a full platform rebuild.

Continuous integration flow propagates update signals through interconnected system paths, triggering module reloads in real time.

We build a complete retrieval-augmented generation pipeline that covers document indexing, semantic retrieval, result reranking, and answer generation with source citations.

Software engineer configuring applications on a laptop surrounded by server monitors in a modern tech workspace

Enterprise search implementation

We implement a unified semantic search layer for all knowledge repositories within your enterprise to provide your employees with a single location to accurately search for permitted information.

 IT team review digital strategies for improving patient care with advanced telemedicine solutions

AI copilot integration

We integrate an AI copilot into your existing corporate system tools, including but not limited to CRM, ERP, Confluence, SharePoint, Jira, etc., so that employees can access an AI assistant from their working environment.

AI as the digital engine driving smart, adaptive experiences in metaverse ecosystems

AI copilot consulting and strategy

We perform analysis of your corporate workflow and document landscapes, and identify where to use an AI copilot. We then define the system architecture and integration path that will best suit your architecture.

IT consultants conducting a project kickoff meeting in a modern glass office

MVP and proof of concept development

We build prototypes to provide proof of practical feasibility and validate answer quality using actual data from your organization, allowing your stakeholders to assess the real AI copilot performance before initiating our full-cycle AI copilot development services.

Writing and reviewing source code in a modern programming environment

End-to-end AI copilot development

We manage the entire lifecycle from capturing requirements and designing secure system architecture to selecting models, building the RAG pipeline, testing the solution, and deploying an AI copilot in your production environment.

Tech specialist focused on coding in an open-plan office environment with digital workstations

AI model training and fine-tuning

We train and fine-tune large language models using your proprietary internal data to provide higher relevance of answers and greater accuracy against the specific terminology, processes, and domain knowledge relevant to your business.

Software engineer integrating ML-driven virtual avatars into enterprise systems for advanced user interaction

Legacy system AI modernization

We integrate AI copilot capabilities into your existing corporate software to replace outdated text search with conversational access to your internal knowledge without a full platform rebuild.

Continuous integration flow propagates update signals through interconnected system paths, triggering module reloads in real time.

RAG system development

We build a complete retrieval-augmented generation pipeline that covers document indexing, semantic retrieval, result reranking, and answer generation with source citations.

Se mer om Software engineer configuring applications on a laptop surrounded by server monitors in a modern tech workspace

Enterprise search implementation

We implement a unified semantic search layer for all knowledge repositories within your enterprise to provide your employees with a single location to accurately search for permitted information.

 IT team review digital strategies for improving patient care with advanced telemedicine solutions

AI copilot integration

We integrate an AI copilot into your existing corporate system tools, including but not limited to CRM, ERP, Confluence, SharePoint, Jira, etc., so that employees can access an AI assistant from their working environment.

AI as the digital engine driving smart, adaptive experiences in metaverse ecosystems
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Hays logo.Spar logo. Tietoevry logo. BS2 logo. Digital science logo. CBQK.QA logo. Topcon logo.NTT Data logo. Momentum Metropolitan logo. Familux Resorts logo. LAPRAAC logo.
Hays logo.Spar logo. Tietoevry logo. BS2 logo. Digital science logo. CBQK.QA logo. Topcon logo.NTT Data logo. Momentum Metropolitan logo. Familux Resorts logo. LAPRAAC logo.
Hays logo.Spar logo. Tietoevry logo. BS2 logo. Digital science logo. CBQK.QA logo.
Hays logo.Spar logo. Tietoevry logo. Digital science logo. CBQK.QA logo.
Topcon logo.NTT Data logo. Momentum Metropolitan logo. Familux Resorts logo. LAPRAAC logo.
Topcon logo.NTT Data logo. Momentum Metropolitan logo. Familux Resorts logo. LAPRAAC logo.

Our AI copilot development solutions

Each solution we deliver targets a specific gap in how your organization accesses, uses, and maintains its internal knowledge. We engineer specialized systems that securely retrieve facts and assist your staff in their familiar workspaces.

  • Embedded copilot

We develop a sidebar or widget embedded in your existing applications and documents that delivers answers to questions related to the content, provides recommendations on actions to take next, opens associated files, and reduces the need for screen switching.

  • Document copilot

We engineer a copilot that scans, summarizes, and compares large numbers of documents in order to extract key data points and supports on-demand creation of structured summaries from data.

  • RAG core system

We implement the backend engine responsible for indexing documents, providing semantic retrieval, reranking results, enforcing user access rights, and generating accurate answers from your corporate data sources.

  • Answer quality console

We deliver a monitoring console with answer accuracy metrics, user feedback collection, and A/B testing for prompts and source configurations to support continuous improvement of the system.

  • Connectors and integrations

We create plug-in point connectors with incremental updates for Confluence, SharePoint, Jira, CRMs, and other storage systems, so copilots always work with up-to-date, secure information and respect existing access rights.

  • Agentisk AI

We build autonomous agents that apply advanced reasoning, so they can query databases and make updates to records across all of your enterprise applications (e.g, Jira, Salesforce, custom tools) and automatically execute sequential activities.

Connect your internal knowledge to an AI copilot your teams will actually use
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Leveranser

Every AI copilot engagement ends with a production-ready system and the assets your team needs to run and evolve it. We hand over all necessary administrative tools and comprehensive summary materials to guarantee independent system maintenance.

Deployed RAG-based copilot
You receive a fully deployed RAG-based AI copilot that provides a central knowledge layer for your company with verified sources and access based on job roles.
Web interface & embedded widget
The engagement delivers a user-ready web interface or an embedded sidebar widget that integrates into your existing workspace to provide employees access to an AI copilot.
API for internal integrations
A documented REST API is provided to connect an AI copilot to your internal applications, so that multiple systems can programmatically access the knowledge layer without requiring manual intervention.
Answer system with citations
A query-and-answer-style system that provides accurate and assignable responses to users who can submit any request, and a confirmation that the user submitting that request has the appropriate permissions.
Admin console for data management
The administration console has functionality to manage the data sources used by an AI copilot, configure indices for imported data, analyze usage metrics to assess service quality, monitor the quality of answers returned by an AI assistant, and suggest improvements.
Documentation & operational runbook
A complete technical documentation set related to the operation, maintenance, addition of new data sources, and scaling of an AI copilot technology solution after it has been implemented.

Most internal knowledge never gets fully used

An AI copilot surfaces the right info at the right moment for every team.

Why choose Innowise as your AI copilot development company

Innowise has delivered 1,600+ projects across more than 40 industries for 19+ years. Our team implements secure methods of vector indexing and strictly adheres to permission control standards to ensure that your new software produces valid results and automates complex internal tasks in no time.

Our AI copilot development process

Every AI copilot is developed with pre-defined checkpoints and deliverables. Consequently, every project has the same expectations for delivery from the time it begins until it goes live.

Discovery & goal alignment

We work directly with your team to identify the broad objectives and the actual use cases that will be addressed by an AI copilot.

Data analysis & preparation

We organize, clean, and format your documents, datasets, and connected API sources, so they can all be properly indexed into the RAG pipeline.

Arkitekturdesign

We design the RAG architecture and identify the vector database, language model, and access policy specifications based on your data and infrastructure.

Prototyping & validation

We create a prototype based on a real sample of your data, and validate the quality of answers before moving into full-scale development.

Utveckling & integration

We build the complete system and integrate it into your infrastructure, connecting it to your existing tools and internal data sources.

Driftsättning & utbildning

We deploy an AI copilot to your production environment and conduct onboarding sessions to help your team become productive from day one.

Monitoring & scaling

We monitor the quality of answers, system performance, and usage patterns after launch, and we scale the copilot as your data and requirements rapidly evolve.

Modeller för engagemang

Dedikerat utvecklingsteam

We assemble a dedicated team of AI developers, engineers, architects, and quality assurance specialists together based on the copilot requirements of your project, and they can start within 1-2 weeks.

Begär ett dedikerat team

Förstärkning av teamet

Our AI engineers help augment an existing development team to fill in any gaps with architecture planning (AIGC), model tuning, and integration support.

Begär personalökning

Project-based delivery

We provide all the necessary requirements for overseeing your whole copilot project, including scoping, architecture, and evaluation after delivery. We provide a project delivery map that has all defined delivery schedules and scope outlined.

Begär projektoutsourcing

Timeline and pricing

AI copilot development projects typically range from 4 weeks and from 8,000 USD, depending on the following factors: amount of data used, level of complexity (in terms of number of systems being integrated), any specific integration requirements, security requirements, and performance benchmarks.

Vad våra kunder tycker

Alla vittnesmål (54)

Innowises arbete uppfyllde alla förväntningar. Teamet var effektivt, snabbt och utöver sina projektleveranser. Kunderna kan förvänta sig ett erfaret team som erbjuder en rad företagstjänster.
Alice Bodnar
COO, FarOut (tidigare Atlas Guides)
5.0
Läs hela recensionen
Se detaljer om projektet
Vi är mer än nöjda med vårt fruktbara samarbete med Innowise, eftersom de utför uppgifter i enlighet med våra höga krav och företagsstandarder och ger önskat resultat.
Stefania Basciu
Leveransdirektör, Topcon Agriculture
4.5
Läs hela recensionen
Se detaljer om projektet
Innowise:s engagemang för att upprätthålla en utmärkt servicestandard var imponerande. De främjade en samarbetsinriktad teammiljö, särskilt under oförutsedda externa utmaningar, vilket var särskilt anmärkningsvärt.
David Roberts
VD, ReVerb
5.0
Läs hela recensionen
Se detaljer om projektet
Team Innowise
See what a custom AI copilot looks like

Get a scoped proposal for your use case from our AI engineering team.

Artsiom Kozak

It is very easy to build an AI copilot that functions well in demo mode. However, creating an AI copilot that complies with permission boundaries, maintains awareness of document changes, and performs successfully every day for hundreds of employees is a completely different engineering problem. That gap is where most implementations fail and where our focus at Innowise sits.

Chef för teknisk expertis för AI

FAQ

An AI copilot is a conversational assistant that is powered by the data available within your organization. This chatbot assists your employees in finding answers to their questions using the same data and documents that they have access to.

A traditional chatbot can only respond to specific questions within a defined scope of scripted questions. They cannot respond to questions that require an understanding of complex, context-based data input. An AI copilot utilizes large language models and retrieval augmented generation technology to understand the complexities of the incoming request and provides a response pulled from actual internal knowledge databases.

RAG stands for retrieval-augmented generation, and it is a method that uses various data sources to retrieve relevant data before generating a response. By using actual documents as the basis for the responses rather than just the general model, an AI copilot can deliver accurate answers supported by source attributions.

An AI copilot catalogs your files, databases, and integrated systems into a vector database and then performs a semantic search looking for the most relevant information corresponding to the requested information. Your data stays within your infrastructure and is never used to train external models.

Yes, and every response generated by the assistant will contain attribution to the file, and/or section used to respond. Employees will have a means by which they can authenticate the response and trace back to the source of the information. This is one of the foundational aspects of the RAG architecture we use on all projects.

Yes, we provide connectors that allow for integration with Confluence, SharePoint, Jira, CRM, ERP, and file storage systems, and we provide a REST API to access copilot from any of your internal applications. The level of integration and access controls is determined during the architecture phase.

We implement a role-based access control system that mimics your current permissions structure. This means that each employee has access only to the data that they are allowed to view. All data processing is done in compliance with security standards as detailed in your compliance guidelines and within your selected infrastructure.

A focused AI copilot deployment typically starts within 4 weeks from the agreed-upon MVP with a limited scope or using a clean data set. Larger initiatives that have multiple backend systems, complex access control requirements, and require organization-wide rollout would take 3–5 months.

Cost factors can include several items, such as volume and complexity of data, number of systems connected, integration, levels of access control, and performance targets. We provide an estimate of potential costs after an initial scoping session at no charge.

Yes, each AI copilot that we create is customized to suit each individual customer's documents, workflows, terminology, and integration environment. There is also custom fine-tuning on proprietary data available to increase the level of accuracy for domain-specific applications.

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