AI-Native Product Engineering
We build customer-facing products where AI is the core experience - co-pilots, generation tools, AI search, conversational interfaces - with streaming UX, error recovery, and evaluation rigour designed in from day one.
Our Services
Generative AI Solutions
Production-grade AI-native products and customer-facing experiences - engineered with RAG architecture, fine-tuning programmes, multimodal pipelines, and the evaluation rigour generative systems require.
70+
Generative AI products shipped to production
240M+
End-user generations served per month at peak
25+
Fine-tuning programmes deployed across model families
8+
Years of applied generative AI engineering experience
Our services
Generative AI Services
Nine generative AI engineering disciplines - from RAG-powered products and fine-tuning programmes to multimodal pipelines, evaluation infrastructure, and AI-native UX - each scoped independently and engineered to enterprise production standards.
RAG-Powered Applications
We engineer retrieval-augmented generation products with hybrid search, reranking, citation grounding, and faithfulness measurement - tuned against your actual users' queries, not generic benchmarks.
Fine-Tuning & Model Customisation
We design fine-tuning programmes (supervised, DPO, RLHF) on Anthropic, OpenAI, and open-source models - with evaluation harnesses that prove the fine-tune outperforms the base model on your specific tasks.
Multimodal Pipelines
We build production systems that combine text, image, audio, and document inputs - with model selection per modality, intelligent routing, and unified observability across the pipeline.
Image & Video Generation Systems
We deploy image and video generation infrastructure with content moderation, safety filtering, attribution tracking, and rights-respecting workflows for brand-safe production use.
Voice & Conversational AI
We engineer voice agents, real-time conversational systems, and TTS/STT pipelines with low-latency streaming, interruption handling, and emotion-aware response design.
AI Search & Discovery
We build vector search, semantic ranking, and AI-native discovery experiences - with hybrid retrieval, query understanding, and grounded responses replacing legacy keyword search.
Code Generation & Developer Tools
We engineer code-generation products, AI-powered IDEs, and developer co-pilots - with code-aware evaluation, secure execution sandboxing, and integration into existing developer workflows.
Evaluation & Quality Infrastructure
We deploy LLM evaluation harnesses, golden datasets, A/B frameworks, and continuous quality monitoring - so generative quality is measured rigorously across every model update, prompt change, and retrieval modification.
Next step
Ready to scope your generative AI product?
Share your use case, target users, and success metrics - 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
Product definition & success metrics
User experience scope, quality acceptance criteria, latency budgets, and evaluation thresholds defined in coordination with your product team before architecture decisions.
02
Model strategy & data audit
Model selection (frontier API, fine-tuned, self-hosted), training data inventory, evaluation set construction, and fine-tuning vs prompting decision documented with trade-offs.
03
Evaluation harness & golden dataset
Production-grade evaluation infrastructure with golden cases, regression detection, A/B framework, and quality dashboards - built before the AI product itself.
04
Production AI product
RAG, generation, multimodal, or fine-tuned system deployed against the evaluation harness - with streaming UX, structured outputs where appropriate, and observability instrumented from day one.
05
Content safety & moderation pipeline
Pre-generation and post-generation moderation, attribution tracking, abuse detection, and human-review queues appropriate for your audience and regulatory context.
06
Operational runbooks & quality monitoring
Documented procedures for model migrations, prompt updates, fine-tune retraining cycles, content incidents, and quality regression handling - handed to your product and ops teams.
Tooling stack
Our Generative AI Technology Stack
Chosen for production reliability, evaluation rigour, and operational track record across enterprise generative AI deployments.
Default stack
Python · TypeScript · Anthropic SDK · Vercel AI SDK · Braintrust
Languages & frameworks
Python
AI development standard
TypeScript
Production runtime
Vercel AI SDK
Streaming UX
Next.js
AI frontend framework
LangGraph
Agent orchestration
LlamaIndex
RAG framework
DSPy
Prompt programming
Pydantic
Structured outputs
FastAPI
AI API framework
Modal
Serverless GPU runtime
Models & providers
Claude
Anthropic frontier
GPT
OpenAI frontier
Gemini
Google frontier
Llama
Open-source frontier
Mistral
Open-source models
Qwen
Multilingual & coding
Stable Diffusion
Image generation
FLUX
Image generation frontier
ElevenLabs
Voice synthesis
Whisper
Speech-to-text
Retrieval, fine-tuning & data
Pinecone
Managed vector DB
Weaviate
Open vector DB
Qdrant
High-performance vector
pgvector
Postgres vectors
Cohere Rerank
Retrieval reranking
Voyage
Embedding models
Unsloth
Fine-tuning toolkit
Axolotl
Fine-tuning framework
Together AI
Fine-tuning hosting
LlamaParse
Complex doc parsing
Evaluation, observability & deployment
Braintrust
LLM evals & logs
LangSmith
Observability
Helicone
LLM monitoring
Arize Phoenix
Open observability
Weights & Biases
Experiment tracking
Replicate
Model deployment
Fireworks
Hosted open models
BentoML
Model serving
vLLM
Self-hosted serving
Pulumi
Infrastructure as code
Trust & diligence
AI Safety & Evaluation Partner Ecosystem
We coordinate AI safety review, content moderation evaluation, and independent quality assessment with recognised firms your stakeholders, regulators, and brand-safety teams already trust - a critical signal for production generative AI products serving end users at scale.
Third-party names and marks belong to their respective owners. Confirm partnership status before publishing.
Partner with us
Built for Teams Where AI Is the Product, Not a Feature.
Generative AI products fail when the model is treated as the differentiator. The model is a moving target - it gets cheaper, faster, and better every quarter, and your users don't care which one is behind your product. What they care about is whether the experience is fast, accurate, safe, and consistent. We build for teams who treat the AI layer as engineering work - with evaluation harnesses, structured outputs where appropriate, content safety pipelines, and the operational rigour that turns probabilistic systems into products customers trust.
Why Bitronix
What Makes Bitronix Different
Not a feature list. Six specific reasons product leaders and engineering teams choose Bitronix for generative AI products that must hold up to user expectations, brand-safety reviews, and the operational realities of probabilistic systems.
01
Evaluation-First Product Development
We build the evaluation harness before the product. Golden datasets, A/B frameworks, and regression detection exist on day one - so you ship with measurable quality, not subjective vibes. When a model provider ships an update or your prompt changes, you find out immediately whether quality moved up or down.
02
Streaming UX Engineering
Generative AI products live or die on perceived latency. We engineer streaming responses, optimistic UI, partial-result rendering, and graceful interruption - so the product feels fast even when the underlying model is slow. Free-text streaming with structured-output reconciliation isn't an afterthought; it's a core engineering discipline.
03
No Black-Box Development
You see every architectural decision, every evaluation result, and every failure mode as we build. Your product, brand-safety, legal, and engineering teams get a live documentation trail they can review at any phase.
04
Model & Provider Agnostic
We deploy across Anthropic, OpenAI, Google, and self-hosted open models - and we know when to fine-tune versus when to prompt versus when to swap providers. The decision is driven by your users' latency, cost, and quality requirements, not by which API we have a partnership with.
05
Brand-Safety & Content Moderation Aware
Generative products that ship without content safety pipelines become PR incidents. We engineer pre-generation and post-generation moderation, abuse detection, attribution tracking, and human-review queues - designed for your specific audience and regulatory context, not as an afterthought toggle.
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 Generative AI Products That Ship and Stay Shipped.
Most generative AI products fail not at launch but at week six - when prompt rot sets in, retrieval drifts against new content, model providers ship updates, and quality regresses without anyone noticing. We engineer the preventable failures out so your AI product compounds value, not surprises.
01
User Journey & Quality Bar Definition
Before architecture decisions, we map the user journey, identify the moments of truth (first generation, complex query, edge-case input), and document the quality bar each moment must clear. Acceptance criteria are measurable - not "the AI should feel smart" but "responses must cite sources for 95% of factual claims with citation accuracy ≥ 92%."
02
Fine-Tune vs Prompt vs RAG Decision
Each approach has costs, capabilities, and failure modes. We document the trade-offs for your specific use case: prompting is fast but ceiling-bound, fine-tuning is capable but requires evaluation infrastructure, RAG is grounded but retrieval-quality-dependent. The decision is documented with rejected alternatives so your engineering team understands why the architecture is what it is.
03
Evaluation Harness Before Product Build
Before the first prompt is written, we build the evaluation harness. Golden datasets are constructed from your real users' queries and your team's expert judgments. Quality metrics - accuracy, faithfulness, citation correctness, latency, cost, safety - are documented and automated.
04
Streaming UX & Latency Engineering
Perceived latency drives generative AI product satisfaction more than absolute latency. We engineer time-to-first-token, partial-rendering strategies, optimistic UI, and graceful interruption - so the product feels responsive at every model size and network condition.
05
Content Safety & Adversarial Testing
Generative products are red-teamed against jailbreaks, prompt injections, brand-safety failures, PII exfiltration, copyright leakage, and abusive use patterns. Failures are documented and bounded with guardrails before launch - not discovered when a journalist finds them.
06
Operational Handoff Pack
Every engagement produces a structured handoff: documented prompts and rationale, evaluation harness with reproducible runs, observability dashboards, content moderation rules, runbooks for prompt updates and model migrations, and a known-limitations document your support and product teams can reference under pressure.
Our methodology is available to review before you engage.
Industries
Generative AI Across Industries
Nine industries where generative AI is creating new product categories, transforming customer-facing experiences, and unlocking value from unstructured content.
Software & Developer Tools
AI-powered IDEs, code-generation products, documentation assistants, and developer co-pilots - engineered for code-aware evaluation, sandboxed execution, and integration into existing developer workflows.
Learn moreMedia & Entertainment
Content generation tools, AI-assisted editing, character generation, and creator co-pilots - with brand-safety pipelines, attribution tracking, and rights-respecting workflows for production use.
Learn moreEducation & Learning
Personalised tutoring systems, AI-graded feedback, content generation for curricula, and adaptive learning experiences - with safety guardrails appropriate for student users and regulatory compatibility.
Learn moreCustomer Experience & Support
AI-native support products, conversational commerce, intelligent help centres, and self-service co-pilots - with citation grounding and graceful escalation to human agents.
Learn moreFinancial Services
Customer-facing financial co-pilots, advisory assistants, document-aware product experiences, and AI-powered client portals - with compliance guardrails and audit trails for regulated environments.
Learn moreHealthcare
Patient-facing health information products, provider-facing clinical co-pilots, and medical content generation - designed for HIPAA compatibility with safety boundaries on diagnostic and treatment guidance.
Learn moreLegal
AI-powered contract products, research co-pilots, and legal document generation - engineered for citation accuracy and attorney-in-the-loop checkpoints on substantive legal output.
Learn moreE-commerce & Retail
AI shopping assistants, product discovery experiences, generative product imagery, and personalised content engines - with brand-safety and inventory-aware grounding.
Learn moreWeb3 & Protocol Operations
Governance summarisation tools, on-chain data co-pilots, and protocol-native AI experiences - for protocol teams shipping AI products to their tokenholder communities.
Learn moreExecution model
Six Phases, One Accountability Chain.
No handoffs that lose context. The team that scopes your generative AI programme ships it and supports it post-launch. Every phase produces a defined artifact - nothing moves forward without it.
Phase 1: Discovery & Product Definition
Timeline: 1-2 weeks
What happens
Product scope, target users, success metrics, latency budgets, and content safety requirements mapped in coordination with your product team before model or architecture decisions.
Deliverables
- Scope document with in/out boundaries
- User journey specification
- Quality bar definition
- Content safety and regulatory constraint register
- Engagement timeline with phase gates
Phase 2: Architecture & Evaluation Design
Timeline: 2-3 weeks
What happens
System architecture, model strategy (fine-tune vs prompt vs RAG), evaluation harness, content moderation pipeline, and integration topology documented. Golden dataset constructed.
Deliverables
- Architecture specification
- Model and approach selection rationale
- Evaluation harness with golden dataset
- Content safety pipeline design
- Integration interface contracts
- Observability plan
Phase 3: Development
Timeline: 3-12 weeks depending on scope
What happens
Generative AI product, RAG pipelines, fine-tuning programmes, multimodal flows, and integrations built against the evaluation harness - with continuous quality measurement and streaming UX validation in CI.
Deliverables
- Production codebase with full documentation
- Evaluation harness running in CI
- Observability instrumented end-to-end
- Content moderation pipeline live
- Internal staging environment matching production
Phase 4: Validation & Adversarial Testing
Timeline: 2-4 weeks
What happens
Red-teaming, jailbreak validation, brand-safety testing, latency and load testing, and user-experience validation run before launch. Findings triaged and remediated against agreed severity SLAs.
Deliverables
- Adversarial test suite with documented attack patterns
- Jailbreak resilience report
- Content safety validation report
- Latency and load test results
- Go/no-go checklist aligned to launch readiness
Phase 5: Launch
Timeline: 1-2 weeks
What happens
Coordinated production deployment, observability go-live, content moderation activation, integration cutover, and human-review queue configuration against explicit launch criteria.
Deliverables
- Deployment record with reproducible builds
- Observability dashboard go-live
- Content moderation 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, prompt regression handling, fine-tune retraining cycles, model migration support, and incident response under defined SLAs.
Deliverables
- Quality and drift monitoring dashboard
- Content safety incident playbook
- Prompt-update and model-migration calendar
- Monthly quality review (optional retainer tier)
- Change request process for product extensions
Timelines assume responsive client feedback at phase gates. Data access provisioning, golden dataset curation, and content safety policy alignment with brand and legal teams are typically the pacing items - programmes targeting a specific launch should engage Discovery 8-12 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, product-minded full-stack engineers, and evaluation owners working exclusively on your generative AI product - suited to flagship customer-facing programmes, multimodal roadmaps, and ongoing quality operations.
Best for: AI-native product roadmaps, regulated customer-facing experiences, continuous model and retrieval iteration
02
Team Extension
1-6 months · 1-3 engineers · Integrated with your team
We embed in your repos and design reviews - you retain product direction; we bring evaluation discipline, streaming UX patterns, and production generative patterns your team is still ramping on.
Best for: Teams shipping a first generative customer experience, co-development with internal AI leads
03
Project-Based
4-16 weeks · Fixed deliverables · Fixed price
Defined scope before kickoff. AI feature builds within existing products, fine-tuning programmes, RAG stand-ups, evaluation harness deployments, and adversarial review engagements are common formats - milestone gates and no billable surprises.
Best for: Targeted pilots, harness stand-ups, content-safety hardening, multimodal proofs of concept
Not sure which model fits? Book a 30-min scoping call → - we'll recommend the right structure based on your team, timeline, and generative AI programme scope.
Case studies
Real work, real results.
Customer-facing co-pilots, developer tools, and evaluation-first generative programmes - case narratives are placeholders; verify against real client work before publishing.
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.
Rich metadata and trader UX layers adjacent to contracts-natural fit for summarisation and monitoring copilots.
Tech stack
- MongoDB
- Express
- React
- Angular
- Node.js
- Solidity
- Chainlink
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.
Content-heavy marketplace where generated copy, moderation assists, and search tuning augment operator throughput.
Tech stack
- MongoDB
- Express
- React
- Node.js
- Solidity
- Ethereum
- IPFS
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.
Structured attestations and policy docs that pair well with retrieval-grounded drafting and checklist agents.
Tech stack
- Solidity
- Hardhat
- IPFS
- TypeScript
- Ethereum
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 parameter programmes where assisted scenario narration complements invariant testing and governance packs.
Tech stack
- Solidity
- Foundry
- Chainlink
- TypeScript
- Ethereum
Google reviews
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Verified feedback from our Google Business Profile.
5.06 Google reviews
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Ready to ship a generative AI product your users will trust?
Share your use case, target users, and launch window - we respond within one business day with a scoped recommendation.
FAQ
Frequently Asked Questions
Straight answers for product, engineering, and procurement teams - before you enter diligence.
The honest answer is that it depends on your use case, your data, your latency budget, your evaluation criteria, and your operational maturity - and any partner who tells you to fine-tune everything (or never fine-tune) is selling a preference, not engineering judgment. As a rough framework: prompt-engineering frontier models is the right default for most use cases - it's fast to ship, easy to iterate, and benefits automatically from model provider improvements; the ceiling is what the base model can do with context. RAG is the right approach when your product needs to ground responses in your specific data (documentation, customer records, knowledge bases) and citation accuracy matters - but RAG quality lives or dies on retrieval quality, not generation quality. Fine-tuning is the right approach when your task has consistent structure (a specific output format, a specific judgment style, a specific tone) and you have evaluation data showing the base model can't reach the quality bar through prompting alone - but fine-tuning requires sustained operational investment in evaluation, retraining cycles, and infrastructure. Most production generative AI products end up using two or three of these approaches together. We document the trade-offs for your specific use case during Phase 1 - including rejected alternatives - so the architecture decision is auditable, not vibes-based. If you're already committed to one approach because of internal constraints, we work within that constraint and flag the limitations honestly.
We treat these as measurable product requirements, not binary promises. Retrieval design, citation formatting, abstention policies, and faithfulness metrics are built into the evaluation harness - with regression alerts when retrieval or model behaviour shifts. We document known failure modes and human-in-the-loop paths where your policy requires them, especially in regulated contexts where outputs augment rather than replace professional judgment.
We work across Anthropic, OpenAI, Google, and self-hosted open-weight stacks - chosen against your latency, cost, compliance, and capability bar. The evaluation harness stays constant so provider or model changes are measurable rather than guesswork.
Yes. We engineer multimodal pipelines with modality-specific model selection, routing, safety layers, and unified observability - including streaming speech interfaces, image and video generation infrastructure with moderation and attribution hooks, and combined text-document-media flows where your UX requires them.
Pre- and post-generation moderation, abuse detection, policy-driven refusals, attribution where outputs derive from third-party content, and human-review queues are scoped to your audience and regulatory context. Residual risk is documented; we do not position moderation as infallible against a motivated adversary.
Yes - supervised, preference, and programme-style fine-tuning where your evaluation data supports it. We only recommend fine-tuning when the harness shows a durable lift on your tasks versus strong prompting and RAG baselines, because fine-tuning adds operational surface area (retraining, eval gates, rollbacks).
Golden datasets from real user queries, automated eval in CI, online metrics (latency, refusal patterns, structured-output validity, citation checks where applicable), and A/B or shadow traffic when rollout risk warrants it. Model, prompt, and retrieval changes ship through the same gates so quality regressions surface as engineering signals, not social-media surprises.
Yes - time-to-first-token, progressive rendering, optimistic UI, cancellation, and partial structured-output reconciliation are standard parts of our frontend and API design for generative products.
Discovery through launch commonly spans roughly 12-26 weeks depending on multimodal scope, eval rigour, content-safety depth, and integration breadth. Core team is typically a lead LLM/product engineer, a full-stack or AI-frontend engineer, and an evaluation owner - scaled with workload.
Product brief, target users, representative queries and content samples, success metrics, latency and cost budgets, content-safety constraints, integrations, compliance context, and target launch window. We respond within one business day with a scoped recommendation.