AI systems that answer, reason, automate and improve real workflows
We design and ship LLM applications, LangChain and LangGraph agents, chatbot training pipelines, retrieval systems and automations that plug into the tools your team already uses.
Useful AI is a product problem before it is a model problem
Most companies do not fail at AI because the model is weak. They fail because the prototype was never connected to the business workflow it was supposed to improve. A chatbot that can answer a demo question is not the same thing as a support assistant that understands policy, asks for missing context, escalates risky cases, logs the outcome and gives managers a clear audit trail. A document summariser is not the same thing as an intake system that checks evidence, drafts a response, routes the task and leaves a human in control. The gap between impressive demo and dependable operation is where our AI development work sits.
Delphro builds AI around the job to be done. We start with the decision, hand-off or repetitive task that costs your team time. Then we define the knowledge sources, permissions, evaluation set, escalation rules, model strategy and integration surface. Some systems need a simple retrieval augmented chatbot with a clean admin panel. Others need multi-step agents, LangGraph state machines, tool calling, queue workers, human review, analytics and regression tests. The right answer is rarely to throw a larger model at the problem; it is to shape the system so the model has the right context, the right tools and the right boundaries.
Production AI also needs discipline after launch. Prompts drift, documents change, users discover edge cases and models evolve. We build with traceability, versioned prompts, test conversations, fallback paths and observability so you can see what the system did and why. That makes AI safer to adopt inside support, sales, operations, HR, logistics, finance and internal knowledge workflows. The result is not a magic layer over your company. It is software that uses language models as one part of a reliable, measurable product.
A complete AI product stack, not a disconnected demo
We can build one focused assistant or a wider automation platform, depending on the workflow.
LLM product strategy
Use-case selection, risk review, model choice, workflow mapping and a written roadmap that separates quick wins from systems that need deeper engineering.
RAG and knowledge assistants
Document ingestion, chunking, embeddings, retrieval, citations, permissions and admin tools so answers are grounded in your actual policies, files and data.
LangChain and LangGraph agents
Tool-using agents with explicit state, branching, retries and human approval gates for workflows that need more than one model call.
Chatbot training and tuning
Conversation design, system prompts, test sets, fallback paths, escalation rules and review loops that improve answer quality without hiding uncertainty.
Workflow automation
AI connected to CRMs, ticketing tools, spreadsheets, email, Slack, WhatsApp, databases and internal APIs so outputs become completed tasks.
Evaluation and guardrails
Golden datasets, hallucination checks, policy filters, confidence thresholds, PII handling and regression testing before prompt or model changes ship.
Dashboards and review queues
Interfaces for operators to approve drafts, correct answers, inspect traces, tag failures and measure impact by workflow, team and channel.
Deployment and operations
Secure hosting, queue workers, logs, cost controls, rate limits, model fallbacks and documentation your team can maintain after launch.
A practical path from workflow to reliable AI
Short discovery, fast prototype, strict evaluation, then production hardening.
Map the workflow
We identify the task, users, source systems, risk level, success metric and approval points before selecting models or frameworks.
Build the evaluation set
Real examples become test conversations, expected answers and failure cases. This gives the project an objective quality bar.
Prototype the system
We wire retrieval, prompts, tools, UI and automations into a working staging product that your team can test with realistic scenarios.
Harden and launch
We add tracing, guardrails, analytics, documentation and support routines, then launch with a measured rollout instead of a big-bang switch.
AI stack
A support agent that reduced repetitive tickets
For a B2B software team, we built a grounded support assistant with escalation rules and a review queue. The system answered routine questions, drafted complex replies and gave managers visibility into weak documentation.
All case studies
AI development questions we hear most
What to expect when moving from AI idea to production system.
Yes. We usually build a private assistant that combines a chat interface, retrieval over approved company knowledge, permission-aware sources, citations, escalation rules and an admin workflow for updating content. The important part is not making it look like ChatGPT; it is making it answer from your documents, respect your policies and hand off safely when confidence is low.
No. A simple assistant may only need direct model calls plus retrieval and a good evaluation set. LangChain helps when many model, retriever and tool components need to be composed. LangGraph becomes valuable when the workflow has explicit state, branches, retries, approvals or long-running steps. We choose the smallest architecture that can stay reliable in production.
We combine grounded retrieval, clear system instructions, source citations, answer constraints, confidence thresholds, refusal rules and test cases built from real examples. We also design the product so uncertain outputs can be reviewed or escalated. Hallucination risk never becomes zero, but it can be measured, reduced and controlled enough for the right class of workflow.
Yes. Tool integration is often where AI becomes valuable. We can connect to CRMs, ticketing systems, email, Slack, WhatsApp, databases, spreadsheets and internal APIs. We add authentication, scopes, logging and approval gates so the assistant can draft, update, route or create records without becoming an uncontrolled automation.
We agree on metrics before build: time saved, ticket deflection, response quality, conversion lift, review acceptance rate, cost per completed task or another business measure. The product then logs outcomes, traces, feedback and exceptions. That gives you a dashboard for actual performance instead of relying on anecdotal reactions to a demo.
Have an AI workflow worth automating? Let's map it
In a free consultation, we will identify the right first use case, the main risks and the shortest route to a useful pilot.
Book a free call