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
Outcomes that move the bottom line
We measure success in business results — revenue, cost, speed, and reliability — not lines of code shipped.
70%
Workload automated
Repetitive research, triage, and data-entry workflows handled end-to-end — freeing your team for higher-value work.
24/7
Always-on operations
Agents work around the clock across time zones, resolving requests and acting on your systems without a night shift.
Days → mins
Faster cycle times
Multi-step processes that took analysts days collapse into minutes with planner–executor agents that call your tools.
Decisions you can audit
Every agent action is traced, cited, and reversible — so leadership trusts the system with real responsibility.
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
Concrete deliverables
Every engagement ships tangible artifacts you own and can run without us — not slideware.
- Agent architecture & system-design document
- Production LangGraph/LangChain build with a typed tool layer
- RAG ingestion pipeline and vector store
- Evaluation suite and prompt regression harness
- Observability dashboards (LangSmith / OpenTelemetry)
- Guardrail policies, runbooks, and team handover
Is this the right fit?
We do our best work when the fit is right. This practice shines for teams like these:
- Support & operations teams drowning in repetitive tickets
- Knowledge work that needs answers grounded in private data
- Back-office processes spanning many tools and systems
- Teams replacing brittle RPA with reasoning-based automation
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.
Ways to work together
Choose the model that matches your stage and risk profile — or tell us your constraints and we'll recommend one.
Defined outcome, fixed budget
Fixed-scope project
A scoped statement of work with milestones, acceptance criteria, and a target launch date. You know the cost and the deliverable before we start.
Best for: Well-defined builds with clear requirements and a firm timeline.
An embedded senior team
Dedicated squad
A cross-functional pod — engineering, design, and delivery — that works as an extension of your team with sustained velocity and full ownership.
Best for: Evolving roadmaps that need ongoing capacity and accountability.
Ongoing partnership
Retainer & support
Reserved monthly capacity for iteration, maintenance, performance work, and on-call reliability — with response-time SLAs that match your risk.
Best for: Maintenance, iteration, and reliability 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.