Production AI agents. Fixed.
WefixAIagentsthatyourteamgaveupon
Your agent hallucinates. Your LLM costs are growing. Your prototype has been "almost production-ready" for months.
We are a focused reliability team that does one thing - diagnose and fix broken AI agents. Not workshops. Not strategy decks. Working code, deployed to production, with reliability commitments.
Free technical assessment - no call required. Results in 72 hours.
Engagement model
Fixed-price engagements
Scope, timeline, and handoff defined up front
Delivery focus
Production in weeks, not months
A deployment playbook built for real operating environments
Coverage
Full-stack: architecture to monitoring
Design, implementation, observability, and team handoff
Live: Agent failure diagnosis
This is the visual center of every engagement: trace, isolate, patch, verify.
Input: "What compliance rules apply to our EU transactions?"
23 chunks retrieved from compliance_docs index. Top similarity: 0.89
Context window at 78% capacity (4820/6144 tokens). 6 chunks dropped.
Output contains 3 claims not grounded in retrieved context. Confidence: 0.41
Skipped - upstream failure detected.
Root cause identified
High confidenceHallucination - context overflow
6 relevant chunks dropped at assembly. LLM filled gaps with fabricated claims.
Failure path detected: retrieval drift → context overflow → unsupported claim generation.
Watch full diagnostic demo47%
Avg cost reduction
across client AI programs
150+
Agents diagnosed
failure taxonomy applied
<72h
First diagnosis
from trace to root cause
12
Enterprise clients
US, UK, EU companies
What we do
Reliability Engineering
Trace, classify, and fix agent failures at the source
Cost Optimization
Model routing, prompt compression, and semantic caching
Production Deployment
Architecture to monitoring, with full team handoff
Frameworks
Models
Infrastructure
Describe your agent's problem.
Watch us diagnose it in real-time.
Our AI analyzes your scenario and provides a preliminary assessment based on common failure patterns we work with.
Describe your agent failure
Or try a common scenario:
AI analysis will appear here
Describe an AI agent problem on the left, and our diagnostic AI will analyze it live
Powered by live reasoning and Nexuron's internal failure analysis framework
Seen this before?
Every broken AI agent
breaks in the same 4 ways.
After working across FinTech, Legal, Healthcare, and SaaS, we have seen the same failure patterns repeat. If your situation matches one of these, we can usually diagnose the bottleneck quickly and move to a concrete fix plan.
You shipped a RAG agent to production.
It answers 85% of queries correctly - but that remaining 15% is destroying user trust. Support tickets tripled. Your team has no systematic way to identify why it fails on specific document types.
How we fix this
We instrument every retrieval step, classify failure patterns by document structure, and build targeted fixes. Not "try a different embedding model" - surgical fixes for each failure mode.
Your agent costs more than the humans it replaced.
Leadership approved the project based on $8K/month projections. You are at $43K/month and climbing. Nobody understands where the tokens are going. The team tried prompt shortening but broke quality.
How we fix this
We trace every token, identify the 3-4 leaks causing 80% of waste, and implement prompt compression + model routing + caching layers. Typical outcome target: lower spend through prompt compression, model routing, and caching without sacrificing quality.
Your agent works in testing but fails in production.
Real users send inputs your test suite never imagined. The agent silently fails on edge cases, returns confident-sounding garbage, and you only find out when a client complains.
How we fix this
We build eval suites from real production failures (not synthetic test data), add output validation guardrails, and implement graceful degradation. Your agent admits uncertainty instead of hallucinating.
You need to go multi-agent but the complexity is overwhelming.
One agent was manageable. Now you need 4 agents collaborating on a workflow, and the failure modes are exponential. Agent A passes bad context to Agent B, which confidently uses it. Debugging is a nightmare.
How we fix this
We design multi-agent architectures with explicit contract boundaries, shared memory management, and end-to-end trace visibility. Each agent has clear failure isolation so one bad output cannot cascade.
Recognize your situation? Let's talk specifics.
Describe your agent's problemWe reply with a diagnosis, not a sales pitch
Agentic AI problems we solve
Every engagement starts with a specific, measurable problem. Here are the ones we hear most.
The uncomfortable truth
Every week you delay,
your competitors pull further ahead.
Agentic AI is not a "nice to have" anymore. Companies that deploy production AI agents today are compounding advantages that will be nearly impossible to catch up to in 18 months. This is not hype - it is basic math. Every automated workflow, every AI-powered customer interaction, every cost optimization compounds daily.
"You hired a large consulting firm for your AI initiative."
Their "AI team" is 3 freshers who completed a LangChain tutorial last month. They are learning on your budget, billing you $300/hour for it, and your agent still hallucinates in production.
"Your in-house team built a prototype that impressed leadership."
That was 4 months ago. It still cannot handle edge cases, LLM costs are 3× the projection, and every model update breaks something. The team is stuck in an endless debugging loop with no systematic framework.
"You are evaluating which framework to adopt - LangChain, CrewAI, or custom."
While you are evaluating, your competitor shipped last quarter. They are already iterating on v2, compounding their data advantage, and locking in customers with AI-powered workflows you have not built yet.
"Your CTO says "we'll figure out AI agents internally.""
Your best engineers are spending 60% of their time debugging LLM failures instead of building product features. That is not a strategic use of $200K+ salaries. The opportunity cost is staggering.
The alternatives are worse
You need an agentic AI specialist.
Not a service company struggling to find one.
The established IT consulting firms are hiring for agentic AI roles themselves - they are competing for the same scarce talent pool you are. When you hire them, you are paying a premium for their brand while their B-team learns on your project. You deserve engineers who have already shipped production agents, not engineers who are about to try for the first time.
Traditional consultancies
Generalists who added "AI" to their pitch deck. Their teams lack hands-on production agent experience. They deliver PowerPoint roadmaps, not working agents.
Offshore dev shops
Low cost, high risk. Agentic AI is not CRUD development - it requires deep understanding of LLM behavior, failure modes, and production reliability. The rework cost exceeds the savings.
Hiring in-house
Agentic AI engineers with production experience are among the rarest talent globally. Average time-to-hire: 4-6 months. Average salary: $250K-$400K. And you need at least 2-3 to build a competent team.
What working with us actually looks like
Thorough analysis first.
Then we move fast - with precision.
We do not rush into implementation to look busy. The first step is always a deep, methodical analysis of your current state - your agent architecture, failure patterns, cost structure, and production gaps. This analysis is what allows us to move with speed and confidence afterward, because we are solving the right problems from day one.
Deep-Dive Analysis & Architecture Review
We instrument your agents, capture execution traces, classify every failure, map your cost structure, and assess production readiness against our 47-point checklist. No guessing - we diagnose with data.
Deliverable: Comprehensive assessment report with prioritized fix roadmap
Targeted Fixes & Reliability Engineering
We fix the highest-impact issues first - the failures that are costing you the most money, the hallucinations that are eroding user trust, the cost leaks that are burning budget. Each fix is validated against your actual failure cases before deployment.
Deliverable: Working fixes deployed to staging, eval suite built from real failures
Production Hardening & Handoff
We add the reliability infrastructure that keeps things working after we leave - monitoring, alerting, cost controls, runbooks. Then we hand off everything to your team with documentation and training. You own the code, the dashboards, and the knowledge.
Deliverable: Production-ready agents, observability stack, team training completed
Timeline varies by scope and complexity. Simple single-agent engagements may complete in 2 weeks. Complex multi-agent systems with compliance requirements typically need 4-6 weeks. We scope honestly and commit to what we can deliver - no inflated timelines, no artificial urgency.
The real cost is not hiring us.
Every month of delay is another month your competitors compound their AI advantage. Every week your engineers spend debugging LLM failures is a week they are not building your next product feature. Every dollar wasted on unoptimized LLM calls is a dollar your competitor is reinvesting into growth.
A 30-minute call with us costs you nothing.
Not having it might cost you the race.
Free · 30 minutes · No pitch, just analysis
How we work with your stack
Real reliability engineering.
Your code, your infrastructure, our reliability patterns.
We instrument what you already run, wire in observability, add guardrails, and leave behind code your team can keep owning after we're gone.
from langsmith import traceablefrom langgraph.graph import StateGraphfrom guardrails import Guardfrom pydantic import BaseModel class Answer(BaseModel): citations: list[str] final_answer: str answer_guard = Guard.for_pydantic(output_class=Answer) @traceable(name="legal-agent.run")def generate_answer(state): docs = retriever.invoke(state["question"]) draft = model.invoke(build_prompt(state, docs)) verified = answer_guard(draft.content) state["citations"] = verified.validated_output.citations state["final_answer"] = verified.validated_output.final_answer state["requires_handoff"] = len(docs) < 3 return stateWhat Nexuron actually changes
Add tracing, enforce structured outputs, gate unsupported answers, surface low-context runs for human handoff, and leave behind evals so the team can keep shipping safely.
Reliability layer
Your framework stays. We strengthen the seams around it.
Typical deliverables
What remains in your repo after an engagement
The Nexuron Reliability Framework
A repair pipeline built
for complex agents under pressure.
Developed across 200+ production debugging sessions. Four phases. One tight feedback loop from visibility to verified reliability.
Trace
We instrument your agent and capture every LLM call, tool invocation, retrieval step, and decision branch.
Classify
We map failures to a structured taxonomy so the real cause is obvious instead of buried in symptoms.
Fix
We patch the exact failure point with code, guardrails, routing logic, and production-ready tests.
Verify
We replay failures under production-like load until the regression suite stays green.
How we improve production systems
Reliability people can feel.
Operational discipline leadership can defend.
We focus on the engineering work that makes AI systems dependable in production: better traces, safer rollouts, tighter cost controls, and cleaner handoff.
Reliability engineering
Trace failures to root cause
Instrument agent runs, retrieval, tool calls, and handoffs so fixes target the actual failure mode instead of symptoms.
Cost controls
Reduce waste before you cut quality
Use prompt compression, model routing, caching, and evals to lower spend without blind regressions.
Production readiness
Ship with observability and rollback paths
Add monitoring, alerting, evaluation gates, and rollout patterns that make launch decisions safer.
Team handoff
Leave your team stronger
Code, dashboards, runbooks, and working sessions stay with your engineers after the engagement.
Who we work with
Focused teams with expensive AI reliability problems
Series B-D startups
US teams racing from AI prototype to revenue-critical rollout.
Regulated fintech
New York and London companies needing traceability and auditability.
Healthcare platforms
PHI-sensitive workflows where grounded outputs are non-negotiable.
Legal and insurance
Document-heavy operations where edge cases quietly destroy trust.
Active client footprint
US, UK, and EU teams with overlapping working hours
Fixed scope. Fixed price. Embedded with your team for 1-6 weeks, aligned to EST, PST, GMT, and CET. You own every line of code, doc, and runbook we ship.
Mid-project check-in
Ready to talk to an engineer instead of another AI agency?
We'll assess your stack, call out the real bottleneck, and tell you honestly if the fastest answer is to keep your current team and skip us.
Schedule Free Strategy Call30 minutes · no commitment · direct with an engineer
Founder-led delivery
A boutique reliability firm.
Senior hands on the hard parts.
Direct access to senior engineers. No layers, no handoffs.
Priya leads Nexuron as a focused, founder-led practice for teams whose AI agents are already in the blast radius of customers, compliance, or real budget pressure. She works directly on traces, evals, routing, guardrails, and the final production patch - the same way a specialist incident engineer would handle a critical outage.
Hands-on founder who works directly inside client codebases and incident traces.
Builds and debugs RAG systems, multi-agent workflows, and reliability guardrails for production teams.
Brings an engineer-first approach: less pitch deck, more root-cause isolation and shipped fixes.
Growing team of specialists
Nexuron stays intentionally small. Specialist partners join when the problem needs deep security, infra, or domain expertise - but clients still get one accountable technical lead throughout the engagement.
GitHub shipping rhythm
Open-source presence plus private client delivery cadence
Why this matters
When an AI workflow is bleeding money or making risky claims, the trust signal isn't headcount. It's direct access to the engineer who can stop the failure quickly and prove the fix.
Questions
30-minute deep-dive with one of our AI infrastructure engineers. We audit your current agent stack, identify reliability gaps, estimate potential cost savings, and recommend a concrete improvement roadmap. No sales pitch. No commitment.
Most engagements begin after a short discovery and scheduling window. If you have an urgent production issue, tell us the context and we will confirm the earliest start we can support.
Yes - LangChain, LangGraph, CrewAI, AutoGen, custom Python/TypeScript agents, RAG pipelines, and multi-step LLM workflows. If your agent makes LLM calls, we can work with it.
Discovery Sprints are $5,000 and credited if you proceed. AI Agent Audits start at $8,000, broader optimization work is scoped after discovery, and managed support is priced around the systems and coverage you need.
Absolutely. Many teams start with a single agent audit or a focused optimization sprint, then expand once the first engagement proves the business case. There is no commitment beyond the initial scope.
Yes, 100%. Everything we build - code, dashboards, runbooks, documentation - is yours. No lock-in, no ongoing dependency.
We typically review prompt design, model routing, caching opportunities, and retrieval efficiency. Any savings estimate is based on your own traffic, quality bar, and evaluation results rather than a generic benchmark.
A reliability audit reviews agent traces, retrieval quality, tool use, guardrails, and outputs to identify the failure patterns that matter most. You get a prioritized fix roadmap, implementation guidance, and a clear production action plan.
We trace every agent run end-to-end - retrieval, context assembly, and generation - to pinpoint whether hallucinations stem from poor retrieval, stale data, context overflow, or insufficient grounding. Then we apply targeted fixes: better chunking, embedding fine-tuning, output guardrails, and factual grounding checks.
Yes. We bring observability, evaluation gates, security hardening, rollout patterns, and cost controls so your team can move from prototype to production faster and with fewer surprises.
Yes. We can help you design audit trails, human oversight workflows, testing approaches, and transparency controls that support your legal and compliance teams.
We work across FinTech, healthcare, insurance, legal tech, e-commerce, and enterprise SaaS. Any organization running AI agents in production - particularly in regulated industries where reliability and compliance are critical.
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