For CTOs, VPs of Engineering, and founders who need to validate, build, and operate AI products without compromising security, scalability, or delivery control. Book an AI Project Consultation
SoftKraft helps B2B teams define the workflow, choose the right model and architecture, integrate data, and measure reliability after launch. We build AI around a business outcome—not a technology trend.
Define the business case, data readiness, architecture, risks, delivery scope, and success metrics before committing to a build.
Build production-ready LLM, agent, RAG, and machine-learning applications—from a focused prototype to an integrated B2B product. Read our guide on how to build a production AI product.
Automate repeatable knowledge-work workflows with AI systems that connect to your business tools and retain human approval where risk requires it.
Use predictive models, NLP, and analytics to turn operational data into forecasts, classifications, and decision support.
We define the user task, baseline, acceptance criteria, and cost constraints before scaling implementation.
We design evaluation, observability, security controls, and fallback paths alongside the AI capability.
We choose LLMs, agents, RAG, or classic ML based on the workflow and data—not the current AI trend.
You get a defined scope, delivery plan, documented decisions, and a team that works with your engineering and product leaders.
We select commercial or open-weight models, agent frameworks, and cloud infrastructure around your data constraints, security requirements, operating cost, and product workflow—not around a preferred vendor.
By implementing ISO 27001 and other certifications, we ensure that our software development services are secure, reliable, and compliant with the highest industry standards.
Our Commitments to YouYou can trust that your data is safe and secure with our ISO 27001 certification and best practices in security and data protection.
You get piece-of-mind with our QA processes that adhere to the highest standards for delivering enterprise-grade software products.
You can count on us to quickly adjust to changes in your project needs and provide engineering talent with the required skills.
We partner with entrepreneurs, business and technology leaders to bring their innovative software-driven products, processes, and business ventures to life.
Explore how we applied AI to supply-chain operations, procurement analytics, and document workflows. Each case shows the business workflow, technical approach, and verified outcome.
Building a scalable, autonomous data engine to index 32,000+ suppliers for a Series Seed startup using Agentic AI
Building a SaaS product that leverages AI/ML to analyze procurement processes for Scope 3 emissions impact
Using Python / Django, React.js, AWS services, and cloud AI-based OCR to design and build a document processing software enabling supplier negotiations and contract management
Define the use case, users, data boundaries, risks, and measurable success criteria.
Validate model quality, integration approach, cost assumptions, and security controls with a focused prototype.
Deliver the production system with testing, CI/CD, observability, and documented handover.
Monitor quality, cost, adoption, and failure modes after launch.
Yes. Our Engineering Managers and AI developers plan scope, milestones, risks, delivery cadence, and technical decisions with your product and engineering leaders.
You retain visibility into priorities and progress while we coordinate design, development, testing, and delivery.
We define acceptance criteria and evaluation scenarios before delivery. During development, we combine automated tests, code reviews, security controls, and monitoring suited to the workflow and data risk.
For systems using external LLMs, we also assess data handling and vendor risk. Read our practical guide to OpenAI data security for business data.
We build LLM and agent workflows with frameworks such as LangGraph, and work with commercial and open-weight models. For private knowledge systems, we implement Retrieval-Augmented Generation (RAG) with vector databases including Pinecone, Weaviate, and PostgreSQL with pgvector.
We use Python for forecasting, classification, and data extraction where classic machine learning fits the problem better than an LLM. Our AI systems run on AWS, Azure, and Google Cloud.
Our recent work includes an agentic supply-chain data engine, a Multi-Model RAG assistant for contract intelligence, and an AI API that automates time tracking through natural language.
We also build document processing, workflow automation, predictive analytics, and data extraction systems.
A focused discovery or working prototype can take days to a few weeks. A production AI system takes longer because scope includes data integration, evaluations, security controls, and operational monitoring.
We provide a delivery plan after assessing the workflow, constraints, and required integrations.
Cost depends on the workflow, data readiness, integrations, security requirements, evaluation scope, and operating model. We provide a detailed estimate after assessing those constraints.
See the AI development cost factors to understand what shapes the budget, then contact us to discuss your scope.
