Generative AI Development for Production-Ready Enterprise Systems
DS Stream designs, builds and operates generative AI development programs that turn LLM pilots into governed, production-grade products: custom AI agents, RAG pipelines, evaluation frameworks and MCP-based integrations into your systems of record. We work on AWS, GCP or Azure and ship the first production-grade use case in 6-10 weeks.
Key takeaways
- DS Stream delivers custom LLM apps, RAG systems and agentic workflows on AWS, GCP or Azure, with 6-10 weeks from discovery to the first production-grade generative AI use case.
- The blocker in most generative AI implementation programs is rarely the model: it is the absence of evaluation standards, grounding discipline, integration governance and a product operating model.
- Every engagement includes an LLMOps foundation: evaluation harness, prompt versioning, guardrails and observability covering token usage, latency p95/p99, groundedness scores and cost per transaction.
- Model routing spans OpenAI, Anthropic, Google, AWS Bedrock and self-hosted open-source LLMs such as Llama and Mistral, so each workload runs on the model that fits it.
- MCP-based tool integration connects generative AI to CRM, ERP, data warehouse and internal APIs, with auditable tool calls that respect your IAM, logging and data residency requirements.
- You own the code, prompts, evaluation datasets and infrastructure; everything is built in your cloud account under your IAM, with documentation, runbooks and knowledge transfer.
Why do most generative AI pilots fail to reach production?
Most organisations already run a GenAI demo: a chatbot, a document summariser, a copilot. Very few run generative AI in software development or customer-facing systems at scale. The blocker is rarely the model. It is missing evaluation standards, grounding discipline, integration governance and an operating model that treats GenAI as a product.
The six failure patterns we see most often:
- Prompt spaghetti - prompts scattered across notebooks, repos and ticketing tools with no versioning or regression testing.
- Hallucination risk - no grounding layer, no retrieval evaluation, no measurable accuracy thresholds before release.
- Integration gap - GenAI apps isolated from CRM, ticketing and the data warehouse, so generative AI for business stays a demo.
- Missing agent discipline - custom AI agents delivered without tool schemas, guardrails or safe escalation.
- Cost surprise - token spend and latency unmonitored until the bill arrives or the UX breaks.
- Compliance stalls - no data residency, PII handling or audit trail for regulated workloads.
What does DS Stream deliver in a generative AI implementation?
We cover the full path from first MVP to enterprise rollout: custom GenAI applications and RAG systems, custom AI agent development and deployment, the LLMOps layer that keeps them operable, MCP integration with your systems of record, and the evaluation and guardrails that gate every release on measurable quality.
End-to-end generative AI software development services
- Custom GenAI applications and RAG systems
- Custom AI agent development and deployment
- LLMOps for generative AI in software development
- MCP integration with enterprise systems
- Evaluation, guardrails and responsible AI
The goal is to ship GenAI apps and agents that your compliance, security and engineering teams will actually approve. Related delivery disciplines are described on our LLMOps and Generative AI Governance pages.
Who is this generative AI service for?
It is built for enterprise data, engineering and operations leaders: CDO or Head of Data & AI, CTO or VP Engineering, Head of Platform or ML Engineering Lead, Head of Customer Operations or CX, plus product leaders and lead engineers who need production-grade patterns instead of disconnected pilots.
- CDO / Head of Data & AI - needs generative AI to leave the lab and become a governed, measurable capability across business units.
- CTO / VP Engineering - needs a scalable platform for generative AI in software development, with clear standards for integration, observability and cost control.
- Head of Platform / ML Engineering Lead - needs reusable LLMOps foundations, evaluation pipelines and deployment discipline so multiple teams can ship GenAI safely.
- Head of Customer Operations / CX - needs generative AI customer support that reduces handle time and deflects tickets, grounded in approved knowledge and integrated with existing systems.
- Product leaders and lead engineers - need production-grade patterns for generative AI app development: prompt versioning, eval-driven development, structured outputs and safe tool use.
Architecture and technical building blocks
Every generative AI implementation is assembled from the same six building blocks, so the platform is reusable across apps and agents.
Model routing layer
- Routing across OpenAI, Anthropic, Google, AWS Bedrock and self-hosted open-source LLMs
- Model choice per use case, including Llama and Mistral
- Model-agnostic architecture on AWS, GCP or Azure
Vector stores
- pgvector, Pinecone, OpenSearch and Vertex AI
- Hybrid search and re-ranking
- Retrieval over approved data sources for grounding
Agent orchestration
- Event-driven orchestration for multi-step agents
- Retries, timeouts and circuit breakers
- Tool schemas, guardrails and safe escalation
Prompt registry and evaluation
- Prompt registry with versioning
- Evaluation harness wired into CI/CD
- Automated regression tests on every model or prompt change
Full observability
- Token usage and cost per transaction
- Latency p95/p99
- Groundedness scores for each release
Data residency and isolation
- Data residency options for regulated workloads
- VPC-isolated deployments
- Auditable tool calls under your IAM and logging
How we deliver generative AI implementation: from discovery to run
Five steps take a shortlist of use cases to a live, monitored production system. The platform work in steps 2 and 3 is reused by every later use case.
Discovery and use case shaping (1-2 weeks)
We qualify 1-3 use cases against business value, data readiness and compliance constraints. Output: prioritised backlog, target SLOs, evaluation criteria and reference architecture.
Platform and LLMOps foundation (2-3 weeks)
We deploy the GenAI platform: model gateway, vector store, prompt registry, evaluation harness and observability. Output: a reusable platform ready for multiple apps and agents.
MVP build and evaluation (3-4 weeks)
We build the first GenAI app or agent, grounded in your data, with automated evaluation and guardrails. Output: a use case hitting pre-agreed accuracy, latency and cost targets.
Production go-live (week 6-10)
We ship to production behind feature flags, run a canary rollout and validate business KPIs. Output: the first implementation live with SLOs, dashboards and on-call runbooks.
Run, optimise and scale
We provide SLA-based support, model refreshes, prompt tuning and enablement, so your teams progressively own the platform and add new use cases without external dependency.
Business outcomes from a production-grade GenAI platform
40-70%
Reduction in manual effort for knowledge-heavy workflows such as support, research and documentation.
Source: DS Stream, Generative AI Development service page
30-50%
Ticket deflection on generative AI for customer support deployments.
Source: DS Stream, Generative AI Development service page
2-5x
Faster time-to-market for new GenAI use cases once the platform is in place.
Source: DS Stream, Generative AI Development service page
30-60%
Lower token and inference cost through model routing and caching.
Source: DS Stream, Generative AI Development service page
6-10 weeks
From kickoff to the first production use case, instead of 6-12 months.
Source: DS Stream, Generative AI Development service page
GenAI app development vs custom AI agent development
| Criterion | GenAI app development | Custom AI agent development |
|---|---|---|
| Interaction pattern | Single-turn or short-turn systems | Multi-step systems that loop until a task is complete |
| What the model does | Produces output in response to a prompt | Plans, calls tools, observes results and loops |
| Typical examples | Summarisers, Q&A, content generation | Task completion with tool use across your systems |
| Extra discipline required | Prompt versioning, grounding, eval-driven development, structured outputs | Tool schemas, guardrails, memory and safe escalation |
| Production deployment | CI/CD with automated evaluation and quality gates | CI/CD plus canary rollout, kill-switches, scoped credentials and SLO monitoring |
Frequently asked questions
How long does a typical generative AI implementation take?
6-10 weeks from discovery to the first production use case. The first 2-3 weeks go into the LLMOps platform and evaluation harness, weeks 3-6 cover the MVP build, and weeks 6-10 cover hardening, canary rollout and go-live. The platform investment is reused, so the second and third apps typically ship in 3-5 weeks.
Do you work with open-source LLMs or only commercial APIs?
Both. We design model-agnostic architectures with a routing layer, so you can mix OpenAI, Anthropic, Google and self-hosted open-source models (Llama, Mistral, Qwen) per use case. Open-source models on your cloud make sense for data residency, cost at scale or fine-tuning; commercial APIs win on frontier capability and time-to-value. We help you decide per workload.
How do you handle hallucinations and accuracy for generative AI for business workloads?
With grounding, evaluation and guardrails. Every use case ships with a retrieval layer over approved data sources, an evaluation dataset with accuracy and groundedness thresholds, automated regression tests on each prompt or model change, and runtime guardrails that block unsafe outputs. Releases are gated by measurable quality, not subjective review.
Can you integrate generative AI with our existing CRM, ERP and data warehouse?
Yes. We use Model Context Protocol (MCP) and standardised tool schemas to integrate with Salesforce, HubSpot, ServiceNow, Zendesk, SAP, Snowflake, BigQuery, Databricks and custom internal APIs. Integrations respect your IAM, logging and data residency requirements, and every tool call is auditable.
What does AI agent deployment look like in production?
AI agent deployment uses the same discipline as any other production service: CI/CD with automated evaluation, canary rollout behind feature flags, SLO-based monitoring of latency, cost, groundedness and tool success rate, plus kill-switches and rollback. Agents run in isolated environments with scoped credentials and are observable from user prompt to tool call to final response.
Do we keep ownership of the code, prompts and models?
Yes. You own the code, prompts, evaluation datasets and infrastructure. Everything is built on your cloud account (AWS, GCP or Azure) under your IAM, and we hand over documentation, runbooks and knowledge transfer so your team can operate independently. We stay engaged through an SLA only as long as you want us to.
What is the difference between generative AI app development and custom AI agent development services?
GenAI app development usually means single-turn or short-turn systems such as summarisers, Q&A and content generation, where the model produces output in response to a prompt. Custom AI agent development covers multi-step systems where the LLM plans, calls tools, observes results and loops until a task is complete, which demands extra discipline around tool schemas, guardrails, memory and safe escalation.
Related services and pages
- LLMOpsThe operational layer behind every implementation: evaluation harness, prompt versioning, guardrails and observability.
- Agentic AI ArchitectureDeeper detail on multi-step agents, tool schemas and event-driven orchestration with retries and circuit breakers.
- Generative AI GovernanceData residency, PII handling and audit trails for regulated GenAI workloads.
- AI EngineeringThe wider AI engineering service family this generative AI development offer belongs to.
- AI FactoryThe operating model that takes GenAI use cases from pilot to governed production across business units.
- All servicesBrowse data engineering, MLOps and AI engineering services that support a GenAI platform.
Ready to move generative AI from pilot to production?
Book a 30-minute, no-obligation scoping call. We will review your use case shortlist, data readiness and target architecture, and give you a realistic implementation plan with timelines, costs and measurable outcomes, whether or not you decide to work with us.
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