Agentic Workflow Development
We build production AI agents with tool use, planning, and recovery - engineered against evaluation harnesses that catch silent regressions before they reach users.
Learn moreOur Services
AI Automation & Agentic Systems
Production-grade AI agents, workflows, and integrations - engineered with evaluation harnesses, observability, and the operational guardrails enterprise systems require.
90+
AI automation systems shipped to production
14M+
Agent and workflow executions per month at peak
35+
Evaluation harnesses deployed for production AI systems
8+
Years of applied AI engineering experience
Our services
AI Automation Services
Nine AI engineering disciplines - from agentic workflows and RAG systems to evaluation infrastructure, model deployment, and human-in-the-loop oversight - each scoped independently and engineered to enterprise production standards.
Next step
Ready to scope your AI programme?
Share your use case, data sources, and target outcomes - we respond within one business day with a scoped recommendation, not a sales pitch.
Delivery scope
Six deliverables, zero ambiguity.
Every engagement produces a defined artifact set. Scope is agreed upfront; nothing is a billable surprise.
01
Use case definition & success metrics
Target outcomes, evaluation criteria, and acceptance thresholds defined in coordination with your operations team before model selection or system architecture decisions.
02
Data audit & retrieval strategy
Source systems, data quality assessment, embedding strategy, and retrieval architecture specified for the domain - with documented gaps and remediation plans.
03
Evaluation harness & golden test set
Production-grade evaluation infrastructure with golden test cases, regression detection, and quality dashboards - built before the AI system itself, not after.
04
Production AI system
Agents, workflows, or RAG systems deployed against the evaluation harness - with structured outputs, error handling, and observability instrumented from day one.
05
Integration & MCP server pipelines
Connectors to your enterprise systems with auth, rate limiting, audit logging, and rollback paths - handed off as code your team can extend.
06
Operational runbooks & quality monitoring
Documented procedures for prompt updates, model migrations, incident response, and quality regression handling - handed to your ops team, not kept in our heads.
Tooling stack
Our AI Automation Technology Stack
Chosen for production reliability, evaluation rigour, and operational track record across enterprise AI deployments.
Default stack
Python · TypeScript · Anthropic SDK · LangGraph · Braintrust
Languages & frameworks
Python
AI development standard
TypeScript
Production runtime
LangGraph
Agent orchestration
LangChain
LLM application framework
LlamaIndex
RAG framework
Pydantic
Structured outputs
FastAPI
AI API framework
Next.js
AI frontend framework
Vercel AI SDK
Streaming UX
DSPy
Prompt programming
Models & providers
Claude
Anthropic frontier
GPT
OpenAI frontier
Gemini
Google frontier
Llama
Open-source models
Mistral
Open-source frontier
Qwen
Multilingual & coding
Ollama
Local inference
vLLM
Self-hosted serving
Fireworks
Hosted open models
Together AI
Open model hosting
Retrieval, vector & data
Pinecone
Managed vector DB
Weaviate
Open vector DB
Qdrant
High-performance vector
pgvector
Postgres vectors
Cohere Rerank
Retrieval reranking
Voyage
Embedding models
ElasticSearch
Hybrid search
Unstructured
Document parsing
LlamaParse
Complex doc parsing
DuckDB
Analytical queries
Evaluation, observability & MLOps
Braintrust
LLM evals & logs
LangSmith
LangChain observability
Helicone
LLM observability
Arize Phoenix
Open observability
Weights & Biases
Experiment tracking
Modal
Serverless GPU
Replicate
Model deployment
BentoML
Model serving
Ray
Distributed compute
Pulumi
Infrastructure as code
Trust & diligence
AI Safety & Evaluation Partner Ecosystem
We coordinate AI safety review, red-teaming, and independent evaluation with recognised firms your stakeholders, regulators, and security teams already trust - a critical signal for production AI deployments in regulated and high-stakes environments.
Third-party names and marks belong to their respective owners. Confirm partnership status before publishing.
Partner with us
Built for Teams Where Silent AI Failures Cost Real Money.
AI systems fail differently than other software. They don't crash - they degrade silently, drift over weeks, and produce confidently wrong outputs that downstream systems treat as authoritative. A misclassified support ticket gets routed wrong. A retrieval system returns plausible-but-stale data. An agent takes an action it shouldn't have. We build for teams who treat AI as production infrastructure with adversarial inputs - with evaluation harnesses, observability, structured outputs, and human-in-the-loop guardrails from day one.
Why Bitronix
What Makes Bitronix Different
Not a feature list. Six specific reasons engineering and operations leaders choose Bitronix for AI programmes that must hold up to silent regressions, drift, and the operational realities of probabilistic systems.
01
Evaluation-First Engineering
We build the evaluation harness before the AI system. Golden test sets, regression detection, and quality dashboards exist on day one - so when the model provider ships an update or a prompt change ships internally, you find out immediately, not three weeks later when a user complains.
02
Structured Outputs By Default
We don't ship AI calls with free-text outputs that downstream systems parse with regex. Pydantic schemas, validated responses, and explicit error states are designed in from day one - so AI output integrates with your existing systems like any other typed API.
03
No Black-Box Development
You see every architectural decision, every evaluation result, and every failure mode as we build. Your engineering, operations, and compliance teams get a live documentation trail they can review at any phase - including the cases where the AI gets it wrong.
04
Model & Provider Agnostic
We deploy across Anthropic, OpenAI, Google, and self-hosted open models - driven by your latency, cost, and compliance requirements, not by our partnership preferences. The evaluation harness is the constant; the model is the variable.
05
Operational Coverage Post-Launch
Most firms ship and disappear. We provide production observability, drift detection, prompt regression alerts, and incident response with defined SLAs - because AI systems don't have launch days, they have continuous quality lifecycles.
06
A Track Record You Can Diligence
Our case studies are public, our tech stacks are listed, and our integrations are named. Read the architecture, check the evaluation methodology, verify the firms. We give you the evidence to decide, not asks to trust.
Engineering methodology
How We Build AI Systems That Don't Degrade Silently.
Most AI failures in production aren't crashes - they're silent regressions, retrieval drift, prompt rot, and confident-but-wrong outputs that downstream systems treat as authoritative. We engineer the preventable ones out so your AI earns operational trust, not surprise post-mortems.
01
Evaluation Harness Before Implementation
Before the first prompt is written, we build the evaluation harness. Golden test cases, edge cases, adversarial inputs, and quality metrics are documented and automated - so every prompt change, model update, or retrieval modification is measured against a consistent baseline. AI quality becomes a regression test, not a vibe check.
02
Retrieval Quality Engineering
RAG systems live or die on retrieval quality, not generation quality. We benchmark retrieval against your actual domain queries - measuring recall, precision, and grounding faithfulness - and tune embedding models, chunking strategies, and reranking against your data, not against generic benchmarks.
03
Structured Output Design
Every AI call ships with Pydantic schemas, retry policies for malformed outputs, and explicit error states. Free-text outputs that downstream systems parse with regex are a known failure pattern; we eliminate them by default.
04
Adversarial Input Testing
We red-team AI systems against the inputs that break them: jailbreaks, prompt injections, PII exfiltration attempts, infinite-loop conversations, deliberately ambiguous queries. Failures are documented and bounded with guardrails before launch - not discovered when a user finds them.
05
Drift Detection & Prompt Regression Monitoring
Production AI systems ship with continuous evaluation against the golden test set. Model provider updates, prompt edits, and retrieval changes are validated automatically - so drift is caught in CI, not in user complaints. Quality dashboards expose regression to your operations team.
06
Operational Handoff Pack
Every engagement produces a structured handoff: documented prompts and rationale, evaluation harness with reproducible runs, observability dashboards, drift detection rules, runbooks for prompt updates and incident response, and a known-limitations document your operations team can reference under pressure.
Our methodology is available to review before you engage.
Industries
AI Automation Across Industries
Nine industries where AI automation is replacing manual workflows, accelerating decisions, and surfacing operational signal hidden in unstructured data.
Financial Services
Trade reconciliation agents, regulatory document analysis, KYC review acceleration, and compliance monitoring - built with structured outputs and audit trails appropriate for regulated environments.
Learn moreHealthcare
Clinical documentation assistants, prior authorization workflows, and provider-coordination automation - designed for HIPAA compatibility with PHI handling guardrails and human-in-the-loop oversight on clinical decisions.
Learn moreLegal
Contract review and analysis agents, discovery acceleration, and matter intake automation - engineered for the precision and citation requirements legal workflows demand, with attorney-in-the-loop checkpoints.
Learn moreLogistics & Supply Chain
Exception handling agents, document extraction from shipping paperwork, and routing optimization - engineered for the volume and edge-case density real logistics operations generate.
Learn moreCustomer Operations
Support agent assistants, ticket triage automation, and quality assurance workflows - designed to handle the long tail of customer queries while routing genuinely novel cases to human agents.
Learn moreSales & Marketing Operations
Lead qualification agents, account research automation, and proposal generation - integrated with CRM and revenue tooling rather than operating as standalone copilots.
Learn moreEngineering & DevOps
Code review agents, incident response copilots, and runbook automation - engineered to integrate with your existing developer tooling rather than replacing it.
Learn moreResearch & Analysis
Document synthesis pipelines, competitive intelligence automation, and structured data extraction from unstructured sources - with citation tracking and grounding verification.
Learn moreWeb3 & Protocol Operations
Governance copilots, treasury operations agents, on-chain monitoring, and proposal analysis - for protocol teams that need AI reasoning over verifiable blockchain state.
Learn moreExecution model
Six Phases, One Accountability Chain.
No handoffs that lose context. The team that scopes your AI programme ships it and supports it post-launch. Every phase produces a defined artifact - nothing moves forward without it.
Phase 1: Discovery & Use Case Definition
Timeline: 1–2 weeks
What happens
Use case scope, target outcomes, success metrics, data sources, and operational constraints mapped in coordination with your operations team before model or architecture decisions.
Deliverables
- Scope document with in/out boundaries
- Success metrics specification
- Data inventory and quality assessment
- Regulatory and compliance constraint register
- Engagement timeline with phase gates
Phase 2: Architecture & Evaluation Design
Timeline: 2–3 weeks
What happens
System architecture, model selection, evaluation harness, and integration topology documented. Golden test set built and acceptance thresholds agreed before implementation.
Deliverables
- Architecture specification
- Model and provider selection rationale
- Evaluation harness with golden test set
- Integration interface contracts
- Observability and drift-detection plan
Phase 3: Development
Timeline: 3–10 weeks depending on scope
What happens
Agents, workflows, RAG systems, and integrations built against the evaluation harness - with continuous quality measurement and structured-output validation in CI.
Deliverables
- Production codebase with full documentation
- Evaluation harness running in CI
- Observability instrumented end-to-end
- Integration services with tests
- Internal staging environment matching production
Phase 4: Validation & Adversarial Testing
Timeline: 2–4 weeks
What happens
Red-teaming, adversarial input testing, jailbreak and prompt-injection validation, drift simulation, and load testing run before launch. Findings triaged and remediated against agreed severity SLAs.
Deliverables
- Adversarial test suite with documented attack patterns
- Jailbreak resilience report
- Load and latency test results
- Drift simulation outcomes
- Go/no-go checklist aligned to operational readiness
Phase 5: Launch
Timeline: 1–2 weeks
What happens
Coordinated production deployment, observability go-live, drift detection activation, integration cutover, and human-in-the-loop checkpoint configuration against explicit launch criteria.
Deliverables
- Deployment record with reproducible builds
- Observability dashboard go-live
- Drift detection rule activation
- Integration cutover log
- Post-launch smoke and synthetic test reports
Phase 6: Support
Timeline: Ongoing - retainer or per-incident
What happens
Quality monitoring, drift detection oversight, prompt regression handling, model migration support, and incident response under defined SLAs.
Deliverables
- Quality and drift monitoring dashboard
- Incident response playbook with severity matrix
- Prompt-update and model-migration calendar
- Monthly quality review (optional retainer tier)
- Change request process for use case extensions
Timelines assume responsive client feedback at phase gates. Data access provisioning, model provider procurement, and evaluation set curation are typically the pacing items - programmes targeting a specific launch should engage Discovery 6–10 weeks before target deployment.
How we partner
Engagement Models
Three ways to engage - structured around how your team works, not how we prefer to sell. Every model operates on the same delivery standard, the same engineering team, and the same accountability chain.
01
Dedicated Development Team
3–12 months · 2–5 engineers · Full-time exclusive
Your programme gets ML engineers, integration specialists, and evaluation owners working exclusively on your agents and workflows - suited to flagship automation programmes and ongoing quality operations.
Best for: Enterprise AI roadmaps, multi-workload agent platforms, regulated environments
02
Team Extension
1–6 months · 1–3 engineers · Integrated with your team
We embed in your repos and ceremonies - you retain product direction; we bring evaluation discipline, integration depth, and production patterns your team is still ramping on.
Best for: Teams shipping a first production agent, co-development with internal AI leads
03
Project-Based
4–16 weeks · Fixed deliverables · Fixed price
Defined scope before kickoff. AI proof-of-concept programmes, evaluation harness builds, and AI system audits are common formats - milestone gates and no billable surprises.
Best for: Targeted pilots, harness stand-ups, adversarial review engagements
Not sure which model fits? Book a 30-min scoping call → - we'll recommend the right structure based on your team, timeline, and AI programme scope.
Case studies
Real work, real results.
Agentic workflows, RAG platforms, and evaluation-first programmes - case narratives are placeholders; verify against real client work before publishing.
Continuum Logistics Exception Engine
Document-extraction and exception-routing agents replacing brittle rule-based systems.
Continuum replaced a maze of human-maintained rules with evaluated agents that read carrier paperwork, classify exceptions, and emit structured records into the TMS stack - with observability on every path.
Exception rate dropped 40% in the first quarter with structured outputs flowing into the existing operations stack.
Tech stack
- Python
- Claude
- LangGraph
- Pydantic
- Braintrust
Northline Compliance Reviewer
Regulatory document analysis with citation grounding and audit trails.
Northline ships an analyst-facing workflow: bulk ingest of filings and policy memos, retrieval over approved corpora, and grounded answers with explicit citations for every flagged clause.
42-hour weekly compliance review cycle reduced to under 4 hours with full citation lineage maintained for every flagged finding.
Tech stack
- Python
- Claude
- LlamaIndex
- Pinecone
- LangSmith
Helix Clinical Co-Pilot
HIPAA-compatible clinical documentation assistant with provider-in-the-loop checkpoints.
Helix assists clinicians with note drafting and coding suggestions while keeping PHI inside approved boundaries - every suggestion is reviewed before it enters the record.
Documentation time reduced 35% per encounter across 4 specialty areas with zero PHI handling exceptions across 9 months.
Tech stack
- Python
- Claude
- pgvector
- FastAPI
- Modal
Citadel Governance Copilot
Governance proposal analysis agent integrating AI reasoning with on-chain treasury state.
Citadel gives delegates concise, sourced briefs on each proposal - tying natural-language analysis to treasury balances and spending authority read from subgraphs.
310 proposals analysed across 11 months with delegate-aligned summaries cited back to source documents and on-chain data.
Tech stack
- Python
- TypeScript
- Claude
- LangGraph
- The Graph
Testimonials
What our clients are Saying
Discover real stories from clients who have improved delivery, audit readiness, and production operations with our team.
Priya Natarajan
VP of Engineering · Continuum Logistics
The AI automation program Bitronix built replaced a tangle of brittle rules with evaluated, observable workflows. Our exception rate dropped by 40% in the first quarter. The team explained trade-offs honestly rather than just telling us what we wanted to hear.
Alexandra Chen
Chief Technology Officer · Northline Markets
Bitronix redesigned our entire settlement architecture. What used to take our ops team four days of manual reconciliation now closes in under fifteen minutes with full audit lineage. The delivery discipline was unlike anything we had seen from an external team.
Daniel Okonkwo
Head of Digital Assets · Helix Capital Partners
We engaged Bitronix to tokenize a $180M real estate portfolio on-chain. They handled investor reporting, compliance checkpoints, and lifecycle events end-to-end. The platform launched on schedule and has processed every redemption without a single incident.
James Whitfield
General Counsel · Meridian DeFi
We needed a smart contract audit that could actually withstand scrutiny from our legal and compliance teams - not just a checkbox report. Bitronix delivered findings with clear severity classification, remediation paths, and documentation our lawyers could read.
Dr. Sarah Mensah
Chief Digital Officer · Veracure Health Systems
Bitronix built our patient data consent layer on a private blockchain in twelve weeks. They understood HIPAA constraints without us having to explain them twice, and the identity integration with our existing IAM stack was seamless. Exactly what a regulated environment requires.
Marcus Liang
CTO · Axiomatic Energy
Our previous vendor gave us a prototype. Bitronix gave us a production system - with runbooks, observability dashboards, and on-call support from day one. Eighteen months in, our blockchain infrastructure has maintained 99.98% uptime across three regions.
Elena Vasquez
Risk & Controls Lead · Summit Treasury
As risk and controls lead, I cared about traceability more than chain hype. Bitronix mapped every privileged role, emergency pause path, and upgrade story into documentation our regulators could follow. That clarity was the win.
Priya Natarajan
VP of Engineering · Continuum Logistics
The AI automation program Bitronix built replaced a tangle of brittle rules with evaluated, observable workflows. Our exception rate dropped by 40% in the first quarter. The team explained trade-offs honestly rather than just telling us what we wanted to hear.
Alexandra Chen
Chief Technology Officer · Northline Markets
Bitronix redesigned our entire settlement architecture. What used to take our ops team four days of manual reconciliation now closes in under fifteen minutes with full audit lineage. The delivery discipline was unlike anything we had seen from an external team.
Daniel Okonkwo
Head of Digital Assets · Helix Capital Partners
We engaged Bitronix to tokenize a $180M real estate portfolio on-chain. They handled investor reporting, compliance checkpoints, and lifecycle events end-to-end. The platform launched on schedule and has processed every redemption without a single incident.
James Whitfield
General Counsel · Meridian DeFi
We needed a smart contract audit that could actually withstand scrutiny from our legal and compliance teams - not just a checkbox report. Bitronix delivered findings with clear severity classification, remediation paths, and documentation our lawyers could read.
Dr. Sarah Mensah
Chief Digital Officer · Veracure Health Systems
Bitronix built our patient data consent layer on a private blockchain in twelve weeks. They understood HIPAA constraints without us having to explain them twice, and the identity integration with our existing IAM stack was seamless. Exactly what a regulated environment requires.
Marcus Liang
CTO · Axiomatic Energy
Our previous vendor gave us a prototype. Bitronix gave us a production system - with runbooks, observability dashboards, and on-call support from day one. Eighteen months in, our blockchain infrastructure has maintained 99.98% uptime across three regions.
Elena Vasquez
Risk & Controls Lead · Summit Treasury
As risk and controls lead, I cared about traceability more than chain hype. Bitronix mapped every privileged role, emergency pause path, and upgrade story into documentation our regulators could follow. That clarity was the win.
Priya Natarajan
VP of Engineering · Continuum Logistics
The AI automation program Bitronix built replaced a tangle of brittle rules with evaluated, observable workflows. Our exception rate dropped by 40% in the first quarter. The team explained trade-offs honestly rather than just telling us what we wanted to hear.
Alexandra Chen
Chief Technology Officer · Northline Markets
Bitronix redesigned our entire settlement architecture. What used to take our ops team four days of manual reconciliation now closes in under fifteen minutes with full audit lineage. The delivery discipline was unlike anything we had seen from an external team.
Daniel Okonkwo
Head of Digital Assets · Helix Capital Partners
We engaged Bitronix to tokenize a $180M real estate portfolio on-chain. They handled investor reporting, compliance checkpoints, and lifecycle events end-to-end. The platform launched on schedule and has processed every redemption without a single incident.
James Whitfield
General Counsel · Meridian DeFi
We needed a smart contract audit that could actually withstand scrutiny from our legal and compliance teams - not just a checkbox report. Bitronix delivered findings with clear severity classification, remediation paths, and documentation our lawyers could read.
Dr. Sarah Mensah
Chief Digital Officer · Veracure Health Systems
Bitronix built our patient data consent layer on a private blockchain in twelve weeks. They understood HIPAA constraints without us having to explain them twice, and the identity integration with our existing IAM stack was seamless. Exactly what a regulated environment requires.
Marcus Liang
CTO · Axiomatic Energy
Our previous vendor gave us a prototype. Bitronix gave us a production system - with runbooks, observability dashboards, and on-call support from day one. Eighteen months in, our blockchain infrastructure has maintained 99.98% uptime across three regions.
Elena Vasquez
Risk & Controls Lead · Summit Treasury
As risk and controls lead, I cared about traceability more than chain hype. Bitronix mapped every privileged role, emergency pause path, and upgrade story into documentation our regulators could follow. That clarity was the win.
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Ready to ship AI your operations team will trust?
Share your use case, data sources, and target outcomes - we respond within one business day with a scoped recommendation.
FAQ
Frequently Asked Questions
Straight answers for engineering, operations, and procurement teams - before you enter diligence.
Both, and the choice should be driven by your latency, cost, compliance, and capability requirements - not by our partnership preferences. We work fluently across Anthropic Claude, OpenAI GPT, Google Gemini, and self-hosted open models (Llama, Mistral, Qwen) deployed on platforms like Modal, vLLM, and Fireworks. For greenfield engagements, we make a model recommendation during Phase 1 based on your specific use case, with documented trade-offs against alternatives. For engagements where you already have a model provider relationship, we build against your existing stack and your existing procurement contracts. Where regulatory or compliance constraints require self-hosted inference, we deploy and operate that infrastructure end-to-end. The constant across every engagement is the evaluation harness - the model provider can change, but how we measure quality stays consistent. If you're considering switching providers mid-engagement (cost, capability, compliance reasons), we can run head-to-head evaluation on your real use case rather than generic benchmarks.
We specialise in operational automation: document workflows, retrieval systems, agentic tools with approvals, voice and chat interfaces with structured handoffs, and integrations into CRM, ITSM, and internal APIs. We avoid positioning AI as the sole decision-maker in regulated domains (clinical diagnosis, legal advice, lending approval) without attorney-, clinician-, or risk-approved human checkpoints - we augment those workflows with citations and structured outputs instead.
We scope data residency, redaction, logging policies, and access controls in Phase 1. Retrieval and tool layers enforce least-privilege access; outputs can be masked or routed for review under your policy. For PHI-aligned workloads we align architecture to your BAA and security reviews - including hosted vs self-hosted inference trade-offs documented before build.
Yes - voice stacks with interruption handling and low-latency paths where your UX requires it; computer-use and browser automation with audit logging and human-in-the-loop gates on sensitive actions. Scope stays explicit about latency budgets, failure modes, and escalation paths.
Golden test sets, automated eval in CI, and production observability (latency, refusal rates, structured-output validation, retrieval grounding checks where applicable). Model or prompt changes ship only after they pass the harness - treating quality like any other regression surface.
Yes. We deploy vLLM/Ollama-style stacks, Modal/Replicate when hosted fits, and VPC-bound inference when policy requires it - with cost, latency, and maintenance trade-offs documented for your stakeholders.
Red-teaming against jailbreaks, injection via tool payloads, and data-exfiltration patterns; tool allowlists; output validators; and operational limits on sensitive tools. Residual risk is documented - we do not promise zero misuse against a motivated adversary.
Yes - OAuth/service accounts, MCP servers where appropriate, rate limits, idempotency, and audit logs. We design rollback and feature-flag cutovers so automation does not strand operators mid-flight.
Discovery through production-ready systems commonly runs 10–22 weeks depending on integration breadth, eval rigour, and adversarial testing scope. Typical core team: lead ML/LLM engineer, integrations engineer, evaluation owner - scaled with workload.
Use case brief, representative data samples (or schema descriptions), systems to integrate, compliance constraints, latency and cost budgets, and target go-live window. We respond within one business day with a scoped recommendation.