Unlock the commercial potential of Artificial Intelligence. We build custom LLM-powered applications, multi-agent workflows, autonomous sales callers, and retrieval-augmented systems (RAG) that automate enterprise operations.
A custom AI development service builds AI systems on your own data and infrastructure: AI agents, LLM applications, RAG knowledge systems and document intelligence, with guardrails, evaluation sets and monitoring. SoftEdge scopes and delivers those systems in two-week sprints, and you own the code and the data from day one.
We build custom AI layers that integrate into your active web platforms and databases, guaranteeing low latency and high accuracy.
Fine-tuning open-source models (Llama-3, Mistral) and integrating ChatGPT/OpenAI APIs securely.
Agentic systems that execute tasks, connect to external APIs, and make logical choices on your behalf.
Workflow automation using n8n and LangChain to automate lead research, customer support, and document summaries.
Context-aware chatbots integrated into WhatsApp, Slack, or website widgets, resolving 80% of support issues.
An AI proof of concept answers “is this possible?”. Production software answers a harder set of questions: where does the data come from, what happens when the model is wrong, who is accountable for the output, and how do we know it is still working next quarter. We build for the second set.
Task-scoped agents that read from and write to your systems through tools and APIs, with permissions and human approval on irreversible actions.
Answers grounded in your documents and databases, with content-level access control and citations back to the source. See RAG development.
Extraction from contracts, claims, clinical records and regulatory dossiers into structured fields, with a review queue for low-confidence items.
Inbound and outbound call handling with latency budgets, escalation to humans and outcome tagging. See voice AI agents.
A large share of AI requests we see should not be AI. If a deterministic rule, a better query or a form with validation solves the problem, that is the cheaper and more reliable answer, and we say so before quoting. Where AI is genuinely the right tool, the work is usually in the data pipeline, permissions and evaluation — not in the prompt.
A chatbot answers questions in a conversation window. An AI agent additionally takes actions in your systems — creating a record, updating a case, booking a slot — through tool calls, under permissions you define, with approval gates on anything irreversible. If no action is required, a well-grounded chatbot is usually the better and cheaper build.
It depends on the number of systems and data sources to integrate, how much guardrail and evaluation work is required, and the compliance bar. Discovery is a free 30-minute call; after it you receive a fixed-price quote with written deliverables, and we flag the cost of any scope change before building it.
Most first AI systems reach production in 6 to 12 weeks. That covers data plumbing, retrieval or tool integration, guardrails, an evaluation set and monitoring. Larger programmes are delivered in phases of 6 to 12 weeks so each phase ships something usable.
Two routes. Where an enterprise API is acceptable, we use provider terms under which your data is not used for training. Where data cannot leave your environment at all, we deploy open-source models inside your own cloud or on-premise infrastructure. Both are decided during discovery, not after.
We build air-gapped private LLM instances on AWS EC2 or utilize zero data-retention API policies to protect user IP.
We use OpenAI GPT-4, Anthropic Claude, Google Gemini, and open-source models like Llama, Mistral, and Phi-3.