Our Services

AI Automation & Agentic Systems

Production-grade AI agents, workflows, and integrations - engineered with evaluation harnesses, observability, and the operational guardrails enterprise systems require.

Book a Strategy Call →View case studies

or download our AI automation readiness checklist →

  • 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.

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.

RAG & Knowledge Retrieval Systems

We engineer retrieval-augmented generation pipelines with hybrid search, reranking, and grounding - tuned against your domain's actual queries, not generic benchmarks.

AI Workflow Orchestration

We design deterministic workflows that wrap probabilistic AI calls - with retry policies, fallback paths, and structured outputs your downstream systems can rely on.

Enterprise Integrations & MCP Servers

We build Model Context Protocol servers and connectors for Salesforce, Slack, Gmail, Jira, databases, and internal tools - with auth, rate limiting, and audit trails appropriate for regulated environments.

Evaluation & Observability Infrastructure

We deploy LLM evaluation harnesses, golden test sets, and production observability - so AI quality is measured continuously, not assessed once at launch.

Voice Agents & Conversational Systems

We engineer voice agents with low-latency speech, interruption handling, and structured tool calling - built for support, sales, and operational use cases that require sub-second response times.

Computer Use & Browser Automation

We build agents that operate browsers, applications, and internal tools through visual reasoning - with audit logging and human-in-the-loop checkpoints for sensitive actions.

Model Deployment & Inference Infrastructure

We deploy fine-tuned and self-hosted models on GPU infrastructure with autoscaling, batching, and cost controls - for latency-sensitive or compliance-bounded workloads.

AI Agents Over On-Chain Data

We build governance copilots, treasury operations agents, and on-chain monitoring systems that integrate AI reasoning with verifiable blockchain state - for protocol teams that need both.

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.

Book a Strategy Call →Schedule a 30-min call

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.

Apollo Research(opens in a new tab)METR(opens in a new tab)Trail of Bits(opens in a new tab)Robust Intelligence(opens in a new tab)HiddenLayer(opens in a new tab)Lakera(opens in a new tab)

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.

Book a Strategy Call →Scope your programme →

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.

Review our case studies →

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 more

Healthcare

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 more

Legal

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 more

Logistics & 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 more

Customer 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 more

Sales & Marketing Operations

Lead qualification agents, account research automation, and proposal generation - integrated with CRM and revenue tooling rather than operating as standalone copilots.

Learn more

Engineering & DevOps

Code review agents, incident response copilots, and runbook automation - engineered to integrate with your existing developer tooling rather than replacing it.

Learn more

Research & Analysis

Document synthesis pipelines, competitive intelligence automation, and structured data extraction from unstructured sources - with citation tracking and grounding verification.

Learn more

Web3 & 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 more

Execution 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.

FinTech

Uwin: A Polymarket-Style Prediction Market with On-Chain Settlement

Polymarket-style prediction market development - outcome-share trading, Chainlink resolution, and collateral accounting on MEAN/MERN + Solidity

Uwin is a custom prediction market platform we built end-to-end, inspired by Polymarket: traders buy and sell outcome shares on real-world events, with transparent resolution rules and deep liquidity across binary and multi-outcome markets. Bitronix delivered the full surface - trader app, operator console, smart contracts, and oracle-backed settlement - rather than skinning a generic template.

Indexing workers and operator dashboards keeping market state consistent with on-chain collateral.

Tech stack

  • MongoDB
  • Express
  • React
  • Angular
  • Node.js
  • Solidity
  • Chainlink
Read case study →
RWA & Tokenisation

Harbor RWA Settlement: On-Chain Rails for Tokenized Real-World Assets

RWA tokenization development - policy-gated minting, NAV oracle quorum, and qualified-custodian segregation on Ethereum

Harbor is on-chain settlement infrastructure we built for tokenizing real-world assets (RWAs). It connects off-chain custody and attestations to transferable reference tokens: mint and burn paths are policy-gated, NAV updates are bound to a signer quorum, and redemption queues stay observable to both issuers and investors. Bitronix engineered the full settlement surface - core contracts, compliance modules, and verification tooling - to mirror fund rules while keeping investor data off-chain.

Policy-gated mint paths and attestations automated across custodian and NAV update workflows.

Tech stack

  • Solidity
  • Hardhat
  • IPFS
  • TypeScript
  • Ethereum
Read case study →
DeFi

Meridian Lending Markets: Isolated-Pool DeFi Lending for Institutional Desks

DeFi lending protocol development - isolated pools, configurable LTV, risk-bounded liquidations, and Chainlink oracle safeguards

Meridian is an isolated-pool DeFi lending protocol we engineered for institutional desks. It pairs aggressive capital efficiency with conservative risk controls: per-asset silos, configurable loan-to-value (LTV) and liquidation bonuses, and predictable auction paths that keep solvency provable under stress. Bitronix delivered the full lending-protocol surface - Solidity markets, oracle safeguards, and a composable liquidation router - built audit-ready from day one.

Risk dashboards and keeper-adjacent flows aligned with oracle heartbeats and liquidation routers.

Tech stack

  • Solidity
  • Foundry
  • Chainlink
  • TypeScript
  • Ethereum
Read case study →
Gaming & Metaverse

NFT Universe: An OpenSea-Class NFT Marketplace, Shipped End-to-End

Custom NFT marketplace development - minting, auctions, royalties, and collection discovery on MERN + Solidity

NFT Universe is a full-featured, production-grade NFT marketplace Bitronix Technologies designed and built for creators and collectors. Rather than reskinning a generic white-label template, we engineered a marketplace with the trading flows users expect from leading venues - wallet onboarding, gas-aware minting, on-chain royalties, live auctions, and an indexer-backed explorer that stays accurate under load.

Indexer-backed marketplace with moderation tooling and featured drops managed without contract rewrites.

Tech stack

  • MongoDB
  • Express
  • React
  • Node.js
  • Solidity
  • Ethereum
  • IPFS
Read case study →
View all case studies →

Google reviews

Hear from our clients

Verified feedback from our Google Business Profile.

Google

Excellent service and communication, fast response and transparent , will do more business in future.

Mohd Shahzad

Google review

Google

A extremely brilliant experience. We are mesmerised by their services and the immense knowledge they have in their field. We certainly look forward to have a great relationship with this company and hope to do many projects together, learning and using their expertise for our business.

M محمد Riaz Vali Sayed

Google review

Google

My experience with Bitronix Technologies was excellent The staff were polite and professional, communication was clear, and the project was completed on schedule. Their transparent pricing and smooth process made everything stress-free. Strongly recommend their services.

Vishakha Chaudhary

Google review

Google

Had a great experience with Bitronix Technologies. They understood my requirements from the very first call. The process was smooth, communication was clear, and the project was delivered on time. Pricing was fair and transparent with no hidden surprises. Highly recommended!

Md Empire

Google review

Google

Worked with Bitronix Technologies for a blockchain project and honestly couldn't be happier. They delivered everything on time, no delays, no excuses - and the pricing was very reasonable for the quality of work. The team clearly knows their stuff. Highly recommend if you're looking for reliable tech development!

Monty Thakur

Google review

Google

I had a very good experience with Bitronix Technologies. Their team is very smart, helpful, and knows their work well. They completed my project on time and the quality of work was really good. Thank you to the whole team for the hard work and support. I would surely recommend them.

Asif Ali

Google review

Google

Excellent service and communication, fast response and transparent , will do more business in future.

Mohd Shahzad

Google review

Google

A extremely brilliant experience. We are mesmerised by their services and the immense knowledge they have in their field. We certainly look forward to have a great relationship with this company and hope to do many projects together, learning and using their expertise for our business.

M محمد Riaz Vali Sayed

Google review

Google

My experience with Bitronix Technologies was excellent The staff were polite and professional, communication was clear, and the project was completed on schedule. Their transparent pricing and smooth process made everything stress-free. Strongly recommend their services.

Vishakha Chaudhary

Google review

Google

Had a great experience with Bitronix Technologies. They understood my requirements from the very first call. The process was smooth, communication was clear, and the project was delivered on time. Pricing was fair and transparent with no hidden surprises. Highly recommended!

Md Empire

Google review

Google

Worked with Bitronix Technologies for a blockchain project and honestly couldn't be happier. They delivered everything on time, no delays, no excuses - and the pricing was very reasonable for the quality of work. The team clearly knows their stuff. Highly recommend if you're looking for reliable tech development!

Monty Thakur

Google review

Google

I had a very good experience with Bitronix Technologies. Their team is very smart, helpful, and knows their work well. They completed my project on time and the quality of work was really good. Thank you to the whole team for the hard work and support. I would surely recommend them.

Asif Ali

Google review

5.06 Google reviews
View on Google →

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Next step

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.

Book a Strategy Call →

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.