AI Agent Development Services
Some automations are trivial. A few Zaps, no logical reasoning. For the more complex cases, there’s our AI agent development company.
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Build custom AI agents with advanced reasoning
Our AI agent development process
AI discovery workshops
We interview stakeholders and run workshops to clearly understand your goals and business context. Our engineers analyze your current systems and integrations, while professional Business Analysts define user needs, technical constraints, and measurable success criteria.
What you get: detailed requirements, use-cases, risk assessment, and cost estimate.
Design & data preprocessing
The next step is to define a viable AI solution that meets the requirements. This includes a feasibility study, feature definition, technology selection, architecture design, as well as planning of scalability, performance, and security measures. The team then collects, validates, and transforms data from structured/unstructured sources. We also label or annotate the data if needed for fine-tuning.
What you get: solution architecture, feasibility assessment, development plan, preprocessing pipeline, prepared datasets, and vector databases.
AI agent development
The team chooses the best LLMs for the task, fine-tunes, and optimizes them for domain-specific accuracy. The fine-tuned models are then evaluated against performance metrics. We then set up a robust retrieval pipeline, LangChain orchestration logic, enterprise integration layer, and the front-end interface users will interact with.
What you get: fine-tuned LLM, RAG layer, secure APIs integration layer, front-end interface, documentation, user acceptance testing (UAT).
Infrastructure setup
DevSecOps engineers provision a robust and secure infrastructure (AWS/Azure/GCP/on-prem) to deploy the AI agent. They implement the best security practices (encryption, IAM, compliance audits) and deploy the agent with Terraform and Kubernetes.
What you get: scalable infrastructure, compliance documentation, security assessment.
Testing and validation
The QA team ensures the accuracy, reliability, and compliance of the AI before it goes live. This step includes functional testing, performance testing, vulnerability scans, penetration tests, and culminates with user acceptance testing (UAT).
What you get: QA reports, performance benchmarks, security, and vulnerability reports.
Deployment and monitoring
The final step begins with the agent going live. We continue monitoring the AI performance and metrics to resolve any issues quickly. The team periodically updates data sources, embeddings, and knowledge bases and fine-tunes the agent based on feedback.
What you get: production-ready AI, support, monitoring dashboards, ongoing updates.
Agentic AI tech stack
AI frameworks and orchestration
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LangChain - TensorFlow
- OpenAI API
- Anthropic Claude 3 API
- Google Gemini API
- Azure OpenAI Service
- LlamaIndex
- Transformers
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Amazon Bedrock - Amazon SageMaker
Biomedical & computer vision
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OpenCV -
MONAI -
NVIDIA Clara - Med-PaLM
- BioBERT
Data management and retrieval
- Snowflake
- Google BigQuery
- Pinecone
- AWS S3
- ChromaDB
- Apache Airflow
- Apache Spark
- Cloudera
- Apache Hadoop
- Apache Hudi
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Amazon EMR -
Amazon Redshift - AWS HealthLake
API gateways and integrations
- Node.js
- Apollo
- AWS API Gateway
- Apache Kafka
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AWS Kinesis - RabbitMQ
- AWS EventBridge
- Zapier
- Workato
CI/CD and DevSecOps
- GitHub Actions
- Kubernetes
- Docker
- AWS ECR
- Terraform
- AWS KMS
- OAuth 2.0
Monitoring and logging
- Datadog
- Prometheus
- Grafana
- LangSmith
- AWS CloudWatch
- Azure Monitor
Leverage industry-specificAI agent solutions
Healthcare
Insurance
Advertising
HR & Recruiting
Finance & Accounting
Supply chain
E-commerce
Retail
Real estate
Information technology
What
our
clients
say
-
Al Hariri
Co-Founder, Vitagene
USA Results-oriented and
outcome-driven«I can tell you confidently that they are different from your regular agency that just wants to charge as much money for their work as they can get away with. MindK is completely results-oriented and outcome-driven.»
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Jesse Raccio
CTO, The Game Band
USA The team is always there to dig in and help
«I’m happy with MindK’s agility, which relates to their communication. If we need to pivot on something, they’re ready to go in a different direction, and it doesn’t take a lot of energy to move that ship. The team is always there to dig in and help us out when we need to understand anything. Overall, they’re really supportive.»
-
Ida Groth
Senior Product Manager, Building Materials Company
Norway Responsibility
and proactiveness«It’s so comforting to know that they see the whole picture and take full responsibility. It’s made all of the difference. I was most impressed with their proactiveness.»
Let's work together!
Send us a few words about your AI challenges. Within 24 hours, we'll contact you to schedule a free non-binding consultation.
FAQ
- How long does it take to launch a custom AI agent?
Initial version in 3-6 weeks for most use cases. More complex deployments with integrations and fine-tuning take 8-12 weeks. We use agile delivery with early access milestones.
What kind of input/data do you need from our side?
- Can your AI agents integrate with our existing systems?
Yes. We offer API-first architecture with connectors for Salesforce, HubSpot, Workday, Greenhouse, Zendesk, and custom internal systems via REST, GraphQL, and event-based triggers.
- How do you protect sensitive data?
All data is encrypted in transit and at rest (AES-256/TLS 1.2+). We support role-based access controls, isolated environments, zero-retention model options, and deploy in client-owned VPCs if required.
- What are the potential savings that result from your AI agent development services?
Clients typically cut response times by 60–80%, reduce manual task load by 40–70%, and eliminate repetitive knowledge retrieval. ROI is usually seen within 6–12 weeks of deployment.
- What kind of input and involvement is expected from our internal team?
Our AI agent development company usually needs access to existing documents (FAQs, SOPs, profiles, APIs), typical user queries, system integration details, and your desired response standards.
We’ll also need 2-4 hours a week from 1-2 stakeholders for feedback, access, and validation.
Our team handles the rest, including data ingestion, infrastructure setup, and end-to-end deployment.
- How do you ensure high accuracy with minimal hallucinations?
We use retrieval-augmented generation (RAG), enforce source-grounded responses, apply hard/soft validation thresholds, and fine-tune models when domain precision is critical. Every output is traceable back to the source.
- What are the limitations of advanced AI agents?
Despite its advantages, employing RAG or similar retrieval-augmented technologies comes with considerations:
Data Quality: requires high-quality, regularly maintained knowledge bases to ensure effective retrieval.
Infrastructure complexity: necessitates additional infrastructure for indexing, retrieval, and storage (e.g., vector databases, embeddings management).
Latency: retrieval processes add latency; optimization is crucial for responsiveness.