Our Services
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
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.
Next step
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
Every engagement produces a defined artifact set. Scope is agreed upfront; nothing is a billable surprise.
User experience scope, quality acceptance criteria, latency budgets, and evaluation thresholds defined in coordination with your product team before architecture decisions.
Model selection (frontier API, fine-tuned, self-hosted), training data inventory, evaluation set construction, and fine-tuning vs prompting decision documented with trade-offs.
Production-grade evaluation infrastructure with golden cases, regression detection, A/B framework, and quality dashboards - built before the AI product itself.
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.
Pre-generation and post-generation moderation, attribution tracking, abuse detection, and human-review queues appropriate for your audience and regulatory context.
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
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
AI development standard
Production runtime
Streaming UX
AI frontend framework
Agent orchestration
RAG framework
Prompt programming
Structured outputs
AI API framework
Serverless GPU runtime
Anthropic frontier
OpenAI frontier
Google frontier
Open-source frontier
Open-source models
Multilingual & coding
Image generation
Image generation frontier
Voice synthesis
Speech-to-text
Managed vector DB
Open vector DB
High-performance vector
Postgres vectors
Retrieval reranking
Embedding models
Fine-tuning toolkit
Fine-tuning framework
Fine-tuning hosting
Complex doc parsing
LLM evals & logs
Observability
LLM monitoring
Open observability
Experiment tracking
Model deployment
Hosted open models
Model serving
Self-hosted serving
Infrastructure as code
Trust & diligence
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
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
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.
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.
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.
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.
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.
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.
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
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.
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%."
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.
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.
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.
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.
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
Nine industries where generative AI is creating new product categories, transforming customer-facing experiences, and unlocking value from unstructured content.
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 moreContent 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 morePersonalised tutoring systems, AI-graded feedback, content generation for curricula, and adaptive learning experiences - with safety guardrails appropriate for student users and regulatory compatibility.
Learn moreAI-native support products, conversational commerce, intelligent help centres, and self-service co-pilots - with citation grounding and graceful escalation to human agents.
Learn moreCustomer-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 morePatient-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 moreAI-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 moreAI shopping assistants, product discovery experiences, generative product imagery, and personalised content engines - with brand-safety and inventory-aware grounding.
Learn moreGovernance 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
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.
Timeline: 1–2 weeks
Product scope, target users, success metrics, latency budgets, and content safety requirements mapped in coordination with your product team before model or architecture decisions.
Timeline: 2–3 weeks
System architecture, model strategy (fine-tune vs prompt vs RAG), evaluation harness, content moderation pipeline, and integration topology documented. Golden dataset constructed.
Timeline: 3–12 weeks depending on scope
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.
Timeline: 2–4 weeks
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.
Timeline: 1–2 weeks
Coordinated production deployment, observability go-live, content moderation activation, integration cutover, and human-review queue configuration against explicit launch criteria.
Timeline: Ongoing - retainer or per-incident
Quality monitoring, drift detection, prompt regression handling, fine-tune retraining cycles, model migration support, and incident response under defined SLAs.
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
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.
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
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
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
Customer-facing co-pilots, developer tools, and evaluation-first generative programmes - case narratives are placeholders; verify against real client work before publishing.
RAG-powered customer support co-pilot with citation grounding and graceful escalation.
Helix routes tier-1 support through a generative co-pilot grounded in policies, macros, and knowledge articles - streaming responses with explicit citations and handoff when confidence or policy requires a human.
Reduced first-response time from 14 minutes to under 30 seconds with citation accuracy maintained at 94% across 11 months of production traffic.
Tech stack
AI-powered code-generation product with sandboxed execution and code-aware evaluation.
Atlas is a developer-facing assistant that proposes edits and runnable snippets inside enterprise IDEs - with sandboxed execution, static checks, and evaluation suites on internal golden repos.
Adopted by 38,000 developers with measured productivity gains of 24% on routine tasks across enterprise rollouts.
Tech stack
Customer-facing financial research co-pilot with document grounding and compliance guardrails.
Northline surfaces a research workspace where retail and advisory users generate briefs grounded in prospectuses, filings, and approved research libraries - citations and lineage exported for audit.
22,000 monthly active users generating research with full citation lineage and zero compliance incidents across 9 months.
Tech stack
Tokenholder-facing governance summarisation product with on-chain data grounding.
Citadel delivers readable proposal briefs and vote-ready context to tokenholders - grounded in forum threads, code diffs, and subgraph-backed treasury state.
310 governance proposals summarised with delegate-trusted accuracy across 18,000 monthly tokenholder readers.
Tech stack
Testimonials
Discover real stories from clients who have improved delivery, audit readiness, and production operations with our team.
[Founder Name]
Head of Product · [Company]
Bitronix shipped our customer-facing AI product with the evaluation discipline of a real engineering team - golden datasets, regression alerts, content safety pipelines on day one. When the underlying model provider shipped an update that quietly broke citation formatting, we knew within minutes. That's the difference between an AI product and an AI demo.
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.
[Founder Name]
Head of Product · [Company]
Bitronix shipped our customer-facing AI product with the evaluation discipline of a real engineering team - golden datasets, regression alerts, content safety pipelines on day one. When the underlying model provider shipped an update that quietly broke citation formatting, we knew within minutes. That's the difference between an AI product and an AI demo.
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.
[Founder Name]
Head of Product · [Company]
Bitronix shipped our customer-facing AI product with the evaluation discipline of a real engineering team - golden datasets, regression alerts, content safety pipelines on day one. When the underlying model provider shipped an update that quietly broke citation formatting, we knew within minutes. That's the difference between an AI product and an AI demo.
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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View serviceNext step
Share your use case, target users, and launch window - we respond within one business day with a scoped recommendation.
FAQ
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.