Does my use case actually need generative AI or would a simpler approach work?

It often happens that a simpler approach, such as ML implementation, is really more suitable for a project, but to know that, we need to analyze your project requirements and expected deliverables first. For example, if the task involves classification, forecasting, or scoring, an ML-powered predictive model is cheaper and easier to explain. Generative AI is recommended when the task is centered around content generation and processing, document summarization, and automation of multi-step processes. We usually identify the right approach during the discovery phase and always explain the reasoning behind our suggestion.

Should I use RAG, fine-tuning, or an AI agent?

The tool choice depends on the task: The core use case for RAG is retrieving knowledge from relevant approved sources and providing corresponding accurate answers Fine-tuning is used when prompting and retrieval don't produce the required output format (i.e., lack of complex domain terminology or needed brand voice) An AI agent is most valuable for multi-step planning and automation of workflows After analyzing your project, assessing your current digital systems, and identifying the use case for GenAI, we suggest the most suitable option out of the three.

How much does a generative AI development project cost?

There is no definite answer, as the final cost depends on the project scope and factors such as documentation readiness, number of systems to be connected with the GenAI model, amount of model usage that the workload generates, etc. We discuss it with you during an interview and often recommend a PoC first as a budget-friendly option that’s quick to launch and easy to iterate on.

How long does it take to develop and ship a generative AI project?

It depends on the scope and complexity of the project but in general, it might take between three to six months. At Kanda, we often recommend starting with a PoC development as it takes between 6-8 weeks and is considered the fastest and most budget-friendly way to validate and test the AI initiative before going full-scale on its development.

How does Kanda ensure the model stays accurate over time?

We build the system to stay accurate on an ongoing basis, not just at launch. Every answer traces back to your actual documents, so it's grounded in something real, and you can see the source for yourself. We keep measuring that accuracy after launch too, so if quality starts slipping, we catch it early. And for anything where a wrong answer would actually cost you, a person reviews it before it goes out. No generative system is 100% error-free, so instead of guessing, you get a measured rate you can track and act on.

What if I don’t have enough data to start an AI project?

This is actually more common than you think and is definitely not a blocker for your AI initiative. At Kanda, we always start every AI project by assessing what you actually have: what exists, where it lives, how current it is, and whether it's usable as-is. From there, we lay out the options that fit your situation: whether that's building a proof of concept on what's already available, filling specific gaps with synthetic data or third-party enrichment, or fixing access and quality issues before the build starts.

How does Kanda protect my data?

It depends on your risk profile, so we decide it in discovery rather than defaulting to one setup. Depending on what you need, that can mean enterprise API terms that exclude your data from provider training, a private deployment on AWS, Azure, or Google Cloud, a self-hosted open-source model where nothing leaves your environment, or masking sensitive data before it ever reaches a model. Keeping data out of training is only part of it, so we also test permissions, retention, and whether anything sensitive could leak back out through the model's own responses. This isn't ad hoc: Kanda is ISO 27001:2022 certified, meaning our security practices are independently audited against an established standard, not something we assemble per project. That's the same discipline we bring to healthcare and life sciences clients operating under strict regulatory requirements, applied here to how your data moves through an AI system.

Generative AI Development Services for Production-Ready Solutions

Engineered into your systems. Measured in production.

Kanda designs and builds generative AI systems that help teams find answers in internal knowledge, automate document-heavy work, and add AI capabilities to their software products. Our generative AI development services take a use case from validation into production, connecting it to the platforms your teams already use, with human review and access controls designed in from the first sprint. 30+ years of production engineering. 100+ applied AI projects. ISO/IEC 27001:2022 certified.

Discuss Your Generative AI Project

Client Spotlight

Trusted by 450+ Clients Worldwide

Generative AI Development Services Built Around Your Business

At Kanda, we understand that generative AI itself is not a whole solution but part of it. When starting a new project, we always begin with a clear assessment of where gen AI actually fits in your business and how it will support the system, not the other way round.

For that, we align the AI initiative with predefined business goals, ground the model’s responses in verified company data, and engineer governance and guardrails from the start. This approach allows us to integrate the model seamlessly into your workflows without disrupting their operation and ensures that the selected AI model delivers the expected results, bringing you scalability, efficiency, and speed it was initially designed for.

Our Generative AI Development Services

Kanda offers end-to-end generative AI development services you can choose based on your current AI readiness. Enter the engagement at any stage, be it use case discovery, model integration, or optimization of your running AI solution.

Generative AI Consulting and Use Case Discovery
Kanda's consultants work closely with your team to identify where generative AI actually fits and where it doesn't, so you don’t spend the budget on a wrong use case.
- A prioritized list of use cases ranked by business impact and technical feasibility
- Data and system audit and a detailed report on recommended improvements
- Build-vs-buy assessment that compares custom and open-source models and their value for your specific business
- Risk and compliance review flagging hallucination exposure, data privacy, and regulatory constraints upfront
Custom Generative AI Application Development
Our custom generative AI development services cover the whole build and apply to a wide range of platforms, ensuring accessibility and accuracy of the AI model across web and mobile.
- Full-stack build that includes UX/UI design, model layer engineering, data pipeline setup and orchestration, and model integration with your system
- Implementation of streaming, structured outputs, and fallback handling to keep the app running in case of a model failure
- Built-in feedback capture so the system gets more accurate the more your team uses it
- Per-user permissions, usage limits, and per-feature cost visibility for better budget management
Generative AI Integration
Kanda connects the generative AI layer to the systems you already run so that retrieved and processed information is always relevant and available across various departments.
- Use of RAG and vector databases to connect the model with your internal data sources
- Request routing and token management through selected APIs and middleware
- MLOps and LLMOps for automated model management to ensure its ongoing accuracy and consistency
- Model fine-tuning with your organizational data to ensure the answers align with your brand voice and value proposition
Generative AI Modernization
Kanda helps you update and optimize your underperforming legacy software with generative AI solutions in a fast, automated, and cost-effective manner, including migration of your system to the cloud.
- Automatic translation of outdated code into modern programming languages with mandatory human review of the output
- Automated testing covering unit tests, documentation creation, and automated validation scripts
- Creation of architectural roadmaps and design of target microservices architecture
- Transparent dependency mapping that highlights connections between the parts of your system
Generative AI Testing and Evaluation
We offer continuous testing and evaluation of the model during and after its implementation to make sure that the delivered results remain accurate, relevant, and reliable in the long run.
- Mandatory HITL for improved bias mitigation and accuracy control
- Use of specialized testing methods and tools designed specifically for GenAI testing
- Definition of evaluation metrics and use of both automatic and manual evaluation methods for accurate ranking
Deployment, LLMOps, Monitoring and Support
Kanda operationalizes the system for your environment and builds the LLMOps that keep it working as models, prompts, and source data change over time.
- Deployment on AWS, Azure, or Google Cloud, or on-premises where data residency requires it the most
- CI/CD deployment with model, prompt, retrieval, and dataset versioning, plus per-task cost tracking
- Automated monitoring for drift in answer accuracy, groundedness, and cost per query, with a rollback feature when a change degrades results

Generative AI Solutions We Build

Each solution we deliver through our generative AI development services is defined by a user, their workflow, source data, connected systems, and a measurable outcome. We don’t build to chase the hype but to make your business operate on a different level and remove technical bottlenecks that slow it down.

RAG and Enterprise Knowledge Solutions
Kanda builds RAG and enterprise knowledge solutions that help organizations access their corporate proprietary data for more accurate decision-making and use the internal knowledge base for improved customer support and reporting.
- Reduced number of hallucinations since the answers are grounded in the proprietary data
- Easier employee access to internal knowledge resources and saves hours of manual search
- Precise extraction of needed insights and generation of corresponding reports
AI Agents and Intelligent Workflow Automation
We ship AI agents that automate multi-step tasks and scale your workflows in a proactive manner while your employees remain the final decision makers.
- Agents connect to your tools and systems, with built-in retries and fallbacks so a single glitch doesn't stall the whole workflow
- Interpretation and processing of complex documents with further summarization
- Independent handling of routine tasks and their coordination in real time
AI Copilots and Virtual Assistants
Kanda builds customer-service assistants, employee knowledge tools, and role-specific copilots that work from the systems each user actually needs, because an assistant that can't see the order history cannot provide relevant and useful information.
- Grounded responses from your help desk, CRM, product database, and internal knowledge sources
- Intent routing, conversation memory, and context management across a session, without one user's context leaking into another's
- Escalation to a person with the full conversation and retrieved context handed over, so your employees already know what the customer requested without double-checking
Content Generation and Personalization
Producing content at scale usually means either slow manual work or generic, off-brand output. We build systems that generate on-brand content and keep a human in the approval loop before anything ships.
- Content generated against your brand voice and guidelines, not a generic model default
- Personalization by audience, channel, or customer segment, pulling from your actual customer and product data
- Approval workflows built in, so nothing publishes without the review step your team already requires
Multimodal AI Solutions
Some problems aren't text problems. Inspection photos, call recordings, scanned documents, and video all carry information a text-only model can't use. We build solutions that work across formats when the use case genuinely calls for it.
- Image analysis for inspection, quality control, or document scanning, connected to the workflow that acts on the result
- Audio processing for call transcription, analysis, or voice-driven interfaces
- Video and image generation where the use case requires it, always paired with the same review and approval controls as text output

Our Generative AI Development Process

Each generative AI development process is unique depending on the client’s AI maturity and business needs. However, we always have a blueprint ready to use as a reliable framework for building up the whole delivery pipeline.

Discovery and Feasibility Assessment

We start with workflow analysis and drafting and defining which documents and systems the solution needs to reach, who would use it, and what a wrong output would cost. Use cases are scored by value, data readiness, and feasibility. Deliverable: Ranked use-case shortlist, current-state workflow, success metrics, and a recommended starting point.

Data Assessment and Architecture Planning

We assess whether your data can support retrieval, fine-tuning, or neither yet. Then we settle two separate questions that define the architecture of the solution: how the model runs (API or self-hosted) and how it gets its knowledge (RAG, fine-tuning, agentic, or a mix). Deliverable: Architecture decision record, data plan, integration map, model shortlist, and the evaluation set the build will be judged against.

PoC or MVP Development

In six to eight weeks, we build a working prototype against your data and put it under real pressure, checking if it retrieves the right information, how often it gets things wrong, how hard it is to connect to your systems, and what it would actually cost to run at volume. Those answers are what decide whether the project is worth scaling and what additional resources it might require. Deliverable: Prototype running against your data, scores on the agreed evaluation set, known failure modes, and a continue, revise, or stop call.

Product Development and Integration

Once confirmed that the prototype functions as expected, we iterate on it unless delivering a full-fledged product, ready to be integrated into your system. For integration, we start with a small part of the application to test how well it connects with your workflows and gradually scale, closely monitoring data security and model performance. Deliverable: Integrated product increment at each milestone, with prompts, models, and data pipelines under version control.

Testing

We run standard functional, integration, and performance testing alongside checks built specifically for generative systems. These checks are aimed at ensuring that the model stays grounded in real data, does not allow data leakage and can hold up under inputs designed to break it. Deliverable: Test results, red-team findings, risk decisions, and production release criteria.

Deployment

We roll out in controlled stages with monitoring implemented from day one. Users get trained on what the system can and can't do, and there's a clear path for reporting mistakes, so those corrections feed back into improving it. Deliverable: Live deployment, staged rollout plan, user guidance, and operating runbook.

Monitoring and Continuous Improvement

After the model is shipped, we do not stop the work. Our team tracks how the system performs against its baseline and implements needed changes and fixes, including prompts, retrieval, and model version updates. Deliverable: Performance reporting, prioritized optimization backlog, and a model-update policy.

Get Your Generative AI Roadmap

Generative AI Use Cases Across Business Functions

Customer Service
- Automated assignment of support assistants to needed data
- Classification of requests and summarization of cases
- Intelligent suggestion of answers and routing exceptions
- Tracking of first-response time and acceptance of drafted replies
Finance and Legal Operations
- Extraction of clauses or invoice fields
- Automated comparison of documents against policy, and summarization of exceptions
- Sourcing of references and validation rules support review, while a person keeps approval for contracts, filings, and payments
- Measurement of cycle time, manual touches, and exceptions caught before downstream systems
Sales and Marketing
- Preparation of account briefs, proposal sections, and campaign variants
- Accurate estimation of preparation time, edit rate, and adoption by the team
- Rapid content creation in correspondence with brand guidelines
- Intelligent audience segmentation
Human Resources and Employee Operations
- Virtual assistants for processing and managing policies, benefits, onboarding, and HR workflows
- Faster and more accurate talent acquisition and automated interview scheduling with selected candidates
- AI-enabled summarization of peer feedback and performance monitoring with further generation of employment documentation
- Creation of personalized career frameworks and recommendations for professional growth
Product and Software Engineering
- Automated coding and refactoring
- AI-powered code scanning for faster testing and debugging
- Generation of API instructions, release notes, code comments
- Translation of legacy programming languages into modern frameworks

Industries We Serve

Technology Stack for Kanda’s Generative AI Solutions

AI & ML Frameworks
TensorFlow, PyTorch, Keras, Scikit-learn, XGBoost, LightGBM, Hugging Face, LangChain, LlamaIndex
Cloud & MLOps
AWS SageMaker, Google Vertex AI, Azure ML, MLflow, Kubeflow, Apache Airflow, Databricks
LLM & GenAI Platforms
OpenAI GPT, Anthropic Claude, Google Gemini, Azure OpenAI, AWS Bedrock, Meta Llama, Mistral
Data & Engineering
Apache Spark, Kafka, dbt, Snowflake, BigQuery, Redshift, Pandas, NumPy, Jupyter

Security, Governance and Responsible Generative AI

Enterprise GenAI introduces three risks traditional software doesn't: it can expose information a user shouldn't see, follow malicious instructions buried in retrieved content, or take an unintended action through a connected tool. Kanda addresses each of these across the full application, not as a bolt-on layer.

Identity and access
Every retrieval and every action respects the permissions your organization already has, tied to source-system roles rather than a separate AI-specific ruleset. A user can't get from the model what they couldn't get from the underlying system.
Data protection
Data is encrypted, secrets are managed, and retention, residency, and deletion follow rules you define, including exactly what does and doesn't get sent to a model provider.
AI-specific defenses
We test for prompt injection and malicious content hidden in retrieved sources, restrict what tools an agent can call, and validate outputs before they reach a user, so the system resists being manipulated into leaking data or taking the wrong action.
Human oversight
High-impact actions require approval, action limits and escalation paths are built in, and when the system isn't confident enough, it defers to a person instead of guessing.
Operational governance
Models, prompts, datasets, and configurations are versioned, with audit logs, monitoring, and rollback in place, so every change is traceable and reversible.

Business Benefits of Generative AI Services

Each benefit below comes with the measure Kanda uses to prove it.

Faster access to internal knowledge

Policies, contracts, and product knowledge become searchable in natural language and accessible across departments, contributing to data-driven decision making.

Automation of document-heavy work

GenAI speeds up such time-consuming processes like document extraction, summarization, drafting, and validation, saving your team hours of manual work.

Lowered costs

The reduction of manual work leads to reduced operational expenses while also allowing human employees to focus on more critical tasks.

Improved and consistent customer service

The use of smart AI assistants not only enables 24/7 customer support but contributes to consistent brand messaging.

Why Choose Kanda as Your Generative AI Development Company?

Kanda follows an AI-native approach backed by a proven track record of successful AI projects, shipped at scale across both industry leaders and startups. We engineer what we believe in: powerful software products driving measurable business outcomes and serving as leverage instead of being a blocker.

Proven delivery scale
30+ years in software engineering, 100+ delivered AI projects.
Cross-functional capacity
850+ engineers across 20+ countries, spanning AI/ML, data, software, UX, QA, and DevOps expertise.
Security embedded from the start
ISO/IEC 27001:2022 certified. Evaluation, access controls, auditability, and human review are treated as requirements, not as add-ons.
Global client base
450+ clients across the world, from startups to global enterprises.

Start Your Generative AI Project

Bring us the workflow, the documents or the data the model would draw on, and the outcome you're after. We'll tell you whether generative AI is the right tool, what it would take to ship it, and where the risk sits.

If a smaller model, a better search index, or no AI at all would serve you better, you'll hear that first. Scope, timing, and estimates follow that review, so they reflect real integration, data, and security requirements.

Discuss Your Generative AI Project With Our Team

Frequently Asked Questions

Contact Us

Tell us about the workflow you want generative AI to support and the outcome you're after. Our team will review your request and follow up to discuss the right starting point, from use case discovery to a PoC or a full build.

Contact Us
+1-617-340-3850 contact@kandasoft.com
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Generative AI Development Services for Production-Ready Solutions

Engineered into your systems. Measured in production.

Client Spotlight

Trusted by 450+ Clients Worldwide

Generative AI Development Services Built Around Your Business

Our Generative AI Development Services

Generative AI Consulting and Use Case Discovery

Custom Generative AI Application Development

Generative AI Integration

Generative AI Modernization

Generative AI Testing and Evaluation

Deployment, LLMOps, Monitoring and Support

Generative AI Solutions We Build

RAG and Enterprise Knowledge Solutions

AI Agents and Intelligent Workflow Automation

AI Copilots and Virtual Assistants

Content Generation and Personalization

Multimodal AI Solutions

Our Generative AI Development Process

Generative AI Use Cases Across Business Functions

Customer Service

Finance and Legal Operations

Sales and Marketing

Human Resources and Employee Operations

Product and Software Engineering