Agentic AI Solutions
Autonomous agents that do real work
We design and ship production agentic systems — RAG pipelines, LangChain orchestration, and fine-tuned models — that automate workflows, answer from your private data, and act on your tools with guardrails.
70%
avg. support ticket deflection
2 wks
to a working prototype
100%
traced & evaluated agent runs
What we deliver
Multi-agent orchestration
Planner–executor and supervisor architectures with LangGraph: tool calling, memory, retries, and human-in-the-loop approval gates.
Tool & API integration
Agents that operate your CRM, ticketing, databases, and internal APIs through typed, permissioned tool layers — including MCP servers.
Evaluation & guardrails
Offline eval suites, regression tests on prompts, output validation, and content filters so agents stay reliable after launch.
Observability
Tracing of every agent step with LangSmith / OpenTelemetry, cost dashboards, and feedback loops to improve quality over time.
Specialized offerings
Answers grounded in your data
RAG (Retrieval-Augmented Generation)
Production retrieval pipelines that turn your docs, wikis, tickets, and databases into accurate, cited answers — with chunking, hybrid search, and reranking tuned to your corpus.
- Ingestion pipelines for PDFs, wikis, databases & SaaS tools
- Hybrid vector + keyword search with reranking
- Citations, freshness controls, and access-aware retrieval
- Eval harnesses measuring faithfulness & answer quality
Orchestration done right
LangChain & LangGraph Engineering
Composable chains and stateful graphs for complex multi-step workflows — built with typed interfaces, streaming, and async execution from day one.
- Stateful multi-agent graphs with checkpoints & recovery
- Custom tools, structured output, and function calling
- Streaming UX with token-level latency budgets
- LangSmith tracing & prompt regression testing
Models that speak your domain
Fine-Tuning & Model Adaptation
Supervised fine-tuning and preference optimization on open-weight and hosted models when prompting alone isn't enough — backed by rigorous data curation and evals.
- Dataset curation, labeling pipelines & synthetic data
- LoRA / QLoRA fine-tuning on open-weight models
- Hosted fine-tunes (OpenAI, Bedrock) where they fit better
- Side-by-side evals vs. prompt-engineering baselines
Tools of the trade
Our process
01
Use-case audit
We map your workflows, data sources, and ROI targets to find where agents genuinely beat traditional automation.
02
Prototype in 2 weeks
A working agent against your real data and tools — so you validate value before committing to a full build.
03
Hardening & evals
Guardrails, eval suites, cost controls, and failure-mode handling turn the prototype into something you can trust.
04
Deploy & improve
Production rollout with tracing and feedback loops; we iterate on quality with you after launch.
Common questions
Usually RAG plus good prompting gets you 90% of the way for knowledge tasks. We recommend fine-tuning only when you need consistent style, domain-specific reasoning, or lower latency/cost at scale — and we prove the lift with side-by-side evals first.
Let's build something
Have a project in mind?
We'd love to hear about it. Tell us what you're building and we'll get back to you within 24 hours.