AI development services that ship production systems, not prototypes
Innostax builds AI integrations, agentic systems, and automation pipelines that run in production — with a dedicated Tech Lead who owns the architecture, the implementation, and the outcome. We've built AI systems for healthcare, finance, and high-growth SaaS. We know what production AI actually requires.
What AI development services does Innostax provide?
Innostax provides AI development services for CTOs, product leaders, and operations teams at B2B SaaS, FinTech, and HealthTech companies — building production-grade LLM integrations, agentic AI systems, workflow automation pipelines, and data engineering infrastructure through dedicated engineering teams led by a senior Tech Lead accountable for delivery and outcomes.
THE PROBLEM
Most AI projects produce impressive demos. Very few produce production systems.
The gap between an AI prototype and a production AI system is where most AI projects fail.
A prototype is straightforward. Pick a model, write some prompts, call an API, show a demo. It works in the right conditions, with the right inputs, at the right scale. It impresses stakeholders. It gets approved.
Then it goes to production. Real users provide inputs the prototype wasn’t designed for. The model hallucinates in ways that are fine in a demo and catastrophic in a compliance-sensitive workflow. Latency that was acceptable in a sandbox is unacceptable in a product that users are paying for.
The prompt that worked perfectly in development produces inconsistent results at scale. The cost model that seemed fine for a demo becomes untenable at production volume.
Production AI requires engineering discipline that most AI demos skip: multi-model orchestration for the tasks each model is actually best at, retrieval-augmented generation that grounds outputs in your actual data, confidence scoring that flags low-reliability outputs before they reach users, and the observability layer that tells you when the system is drifting from the behaviour it was designed for.
Innostax builds AI systems designed for production from the start — not prototypes that need to be rebuilt before they can be shipped.
AI Solutions & Capabilities
Find the right AI capability for your situation.
Every engagement is different. Use the links below to explore focused pages for this service.
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AI Integration & LLM Apps
Connect LLMs to your existing product — OpenAI, Anthropic Claude, Google Gemini — with the retrieval, orchestration, and guardrail architecture that makes AI features reliable in production. For teams adding AI capabilities to existing software without rebuilding from scratch.
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Agentic AI Development
AI systems that don't just respond — they act. Multi-step agents that reason across tools, APIs, and data sources to complete complex tasks autonomously. For teams building AI that does work, not just AI that answers questions.
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Workflow Automation
AI-powered automation that replaces manual processes — document processing, data extraction, decision routing, scheduled workflows — with the reliability and auditability that business operations require. For teams where manual processes are a cost centre and a scaling constraint.
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Data Engineering & Analytics
The data infrastructure that AI systems run on — pipelines, warehouses, vector databases, and the data quality layer that determines whether your AI produces reliable outputs. For teams whose AI ambitions are constrained by the state of their data.
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How Innostax builds AI systems
Production AI requires engineering decisions that most vendors skip.
Multi-model orchestration
The right model for the right task. No single AI model is best at everything. Claude 3.5 is exceptional for complex reasoning and large context windows. GPT-4o is optimised for rapid completions and embeddings. Gemini excels at high-throughput processing. Production AI systems use the right model for each task in the pipeline — not the same model for everything because it’s simpler to manage.
Innostax builds provider-agnostic
LLM architectures that route tasks to the appropriate model based on complexity, latency requirements, and cost — and that can switch models when a better option becomes available without rebuilding the system.
Plan-Before-Build on every AI feature
Before any AI feature is implemented, the Tech Lead leads a structured planning process — defining the task the AI is performing, the models and retrieval strategy, the failure modes and how they’re handled, the confidence thresholds that determine when the system escalates to a human, and the evaluation criteria that determine whether the feature is working. This step is what prevents the prototype-to-production gap from becoming a rebuild.
Retrieval-Augmented Generation (RAG)
RAG grounded in your data. LLMs hallucinate. The mitigation is grounding — giving the model access to your actual data through retrieval, so its outputs are based on what you know rather than what it was trained on. Innostax builds RAG systems with the vector database architecture, embedding strategy, and retrieval quality evaluation that makes AI outputs reliable in production.
Confidence scoring and human escalation
Production AI systems need to know when they don’t know. We build confidence scoring into every AI pipeline — outputs below the confidence threshold are flagged for human review rather than passed through to users. In regulated industries (FinTech, HealthTech), this isn’t optional.
Observability for AI systems
AI systems drift in ways that application code doesn’t — model behaviour changes with updates, retrieval quality degrades as data changes, prompt performance varies with input distribution. We build observability into AI systems from the start: output logging, quality metrics, drift detection, and the alerting that tells you when the system is behaving differently from how it was designed.
Security and compliance by design
For HealthTech and FinTech, AI systems handle sensitive data that has regulatory implications. HIPAA compliance, end-to-end encryption, data residency requirements, and access controls are designed into the architecture from the start — not retrofitted when a compliance review finds the gaps.
Where AI creates the most value — and where it doesn't.
Where AI creates clear value:
- Processes that are currently manual, repetitive, and high-volume — document processing, data extraction, classification, routing
- Products that need to personalise at a scale that rules-based systems can't achieve
- Workflows where real-time analysis of unstructured data (calls, documents, user behaviour) creates competitive advantage
- Systems that need to reason across large amounts of information to produce a structured output
Where AI adds complexity without proportionate value:
- Simple rule-based decisions that don't benefit from probabilistic reasoning
- Low-volume processes where the implementation cost exceeds the automation value
- Situations where hallucination risk is unacceptable and the retrieval and validation architecture to mitigate it isn't justified by the use case
Production AI systems, on the record.
Real-time AI coaching for sales calls
A multi-model AI system for live sales call analysis — AssemblyAI for streaming transcription during active calls, WhisperX for high-accuracy post-call processing, and Claude 3.5 for real-time objection detection and coaching feedback delivered via SignalR with zero-latency push to the agent’s dashboard.
The system detects “critical turns” in live conversations — client objections, specific questions — and generates resolution suggestions grounded in the client’s internal knowledge base. Mid-call corrections, compliance monitoring, and post-call interactive AI analysis — all in a single production system. Three AI providers, each used for what it does best.
Agentic AI patient communication for healthcare
An agentic AI system that handles automated patient outreach for medication reminders, test scheduling, and care follow-ups. AI agents conduct natural, personalised conversations with patients — indistinguishable from care executives — and generate structured call summaries for care owners.
Built on a HIPAA-compliant microservices architecture on AWS, with end-to-end encryption, encrypted storage, and data residency controls. Agentic AI in a regulated environment where the compliance requirements are non-negotiable.
AI-powered candidate matching at scale
Semantic candidate-job matching using Hugging Face sentence transformers, Azure AI Foundry for heavy processing, and custom OpenAI models for profile generation and enhancement. Vector embeddings in PostgreSQL and Elasticsearch power similarity scoring that explains why a candidate matches a role, not just whether they do. Geospatial proximity search handles location-based matching across thousands of jobs.
Multi-model document processing for banking compliance
An automated compliance checklist system for property appraisal review — replacing a manual process with an AI pipeline that processes appraisal documents, fills compliance checklists, and provides evidence for every answer.
The pipeline uses multiple models via AWS Bedrock — Amazon Nova for interactive reasoning, Writer Palmyra X5 for document understanding, and the Claude family for complex reasoning and multi-step validation. Multi-step validation across models catches low-confidence outputs before they reach reviewers. End-to-end encryption and event-driven architecture ensure compliance and auditability.
Built for teams who need AI that works in production, not just in a demo.
CTOs and engineering leads at B2B SaaS companies adding AI features to an existing product.
You’ve seen the demos. You know what’s possible. You need an engineering team that can build AI features that are reliable in production — with the architecture to handle edge cases, the observability to detect when they’re not, and the Tech Lead who owns the outcome.
Product leaders at FinTech and HealthTech companies
where AI systems handle sensitive data in regulated environments. You need AI built with compliance requirements designed in from the start — HIPAA, SOC 2, data residency — not retrofitted after a security review finds the gaps.
Operations leaders
whose teams are spending significant time on manual processes that AI could automate. Document processing, data extraction, decision routing, scheduled outreach — the workflows where AI automation delivers measurable cost reduction and the reliability that business operations require.
Non-technical founders building AI-native products.
You have the product vision. You need a Tech Lead who can translate it into an architecture that works in production — and who will tell you when the vision needs to be adjusted to account for what AI can actually do reliably.
Two weeks to see whether we build AI that works in production or AI that works in a demo.
2-week free trial on real work.
Your use case, your data, your actual system. You’ll see within two weeks whether the Tech Lead makes the right architecture decisions and whether the AI feature we build holds up under real conditions. If it doesn’t, walk away. No invoice.
1-day termination notice.
If the engagement isn’t delivering production-grade AI, you’re out tomorrow. No lock-in, no notice periods. AI projects that require contract lock-in to stay accountable aren’t projects you want to be locked into.
Engineers who stay for the full build.
Great Place to Work certified — the engineer who designs your AI architecture in month one is still accountable for it in month six. AI systems require continuity. An engineer who has to rediscover your data model, your retrieval architecture, and your prompt engineering every quarter isn’t improving the system — they’re maintaining familiarity with it.
Comparison: production AI vs. the alternatives
Why production AI engineering is different from what most vendors deliver.
| Innostax AI Engineering | AI agency / consultancy | Internal build | Off-the-shelf AI tools | |
|---|---|---|---|---|
| Architecture ownership | Tech Lead owns end-to-end | Delivered and handed off | Depends on team | None — you configure |
| Multi-model orchestration | Yes — right model per task | Varies | Varies | Single model |
| RAG / grounding | Yes — built to your data | Varies | Depends | Limited |
| Compliance (HIPAA, SOC 2) | Designed in from start | Often retrofitted | Depends | Vendor-dependent |
| Observability | Built in — drift detection | Rarely included | Depends | None |
| Ongoing accountability | 1-day exit, continuous | Project-based | N/A | Subscription |
| Trial available | 2 weeks, real work | Rarely | N/A | Free tier only |
FAQ about AI development services
AI integration connects AI capabilities to an existing product — adding an LLM-powered feature, building a RAG system on top of your existing data, or automating a workflow within your current architecture. Building an AI product from scratch means designing the full system — data infrastructure, model selection, orchestration, and application layer — as a new product. Most of our engagements are integrations: adding AI capabilities to systems that already exist. We do both.
We evaluate models against the specific tasks in your pipeline — reasoning complexity, context window requirements, latency constraints, cost, and the compliance requirements of your industry. We build provider-agnostic architectures so the model choice can be updated as the landscape evolves without rebuilding the system. We don't recommend a model because it's the newest or most widely discussed — we recommend it because it's the right tool for the specific task.
Through a combination of retrieval-augmented generation (grounding outputs in your actual data), confidence scoring (flagging low-reliability outputs for human review rather than passing them through), and multi-step validation (using multiple models to cross-check outputs on high-stakes tasks). The right mitigation depends on the use case and the acceptable failure rate — we'll be direct about what's achievable for your specific application.
For HealthTech (HIPAA) and FinTech (SOC 2, financial regulations), compliance requirements are designed into the architecture from the start — data residency, encryption at rest and in transit, access controls, audit logging, and AI agent security checks. We've built HIPAA-compliant AI systems on AWS with end-to-end encryption and data residency controls. Compliance isn't a retrofit — it's an architectural constraint we design around.
Discovery first — understanding the use case, the data, the failure modes, and whether AI is the right solution. Architecture design — model selection, retrieval strategy, orchestration design, confidence thresholds, and evaluation criteria. Implementation — built iteratively, with evaluation at each stage. Production deployment — with observability, monitoring, and the alerting that tells you when the system is drifting. Ongoing optimisation — prompt tuning, retrieval quality improvement, model updates as the landscape evolves.
It depends on the complexity of the use case and the state of your data infrastructure. A focused LLM integration — adding AI-powered features to an existing product with clean data — can be production-ready in four to eight weeks. A full agentic system with multi-model orchestration, RAG, and compliance requirements typically takes three to six months. We'll give you a realistic timeline after the discovery phase, not before.