Most GenAI vendors deliver a prototype and leave. Scrums.com deploys a permanent AI engineering team that ships GenAI to production and maintains it through model updates, prompt drift, and changing requirements. First sprint in under 21 days.
Generative AI development services are dedicated AI engineering teams that design, build, and deploy LLM-powered products, custom AI agents, and intelligent workflow systems on your behalf. Scrums.com provides those teams through our SEOP platform, with model-agnostic expertise, senior-led squads, and full integration with your existing tools and sprint cadence from day one.
Build LLM-powered products from scratch: document intelligence systems, conversational interfaces, content generation engines, and knowledge retrieval applications. Architecture through deployment, production hardening included.
Autonomous AI agents for multi-step workflows, document processing, compliance monitoring, and customer support, governed through our AI Agent Gateway for enterprise-grade control.
Integrate LLM capabilities into existing platforms via API or self-hosted deployment. Fine-tune open-source models (Llama, Mistral) on your proprietary data where off-the-shelf accuracy falls short.
Connect LLMs to your proprietary knowledge base for grounded, verifiable responses. We build vector database infrastructure (Pinecone, Weaviate, pgvector), ingestion pipelines, and re-ranking layers.
Automate workflows requiring natural language understanding: contract analysis, regulatory document review, customer communication drafting, report generation, and knowledge base maintenance.
Ongoing model monitoring, prompt optimization, RAG corpus refresh, and version migration as providers release updates. Token costs and latency tracked as first-class delivery metrics through SEOP.
Most GenAI teams are built to prototype, not to maintain. Scrums.com deploys permanent AI engineering teams with the prompt engineering, compliance architecture, and model operations to keep GenAI performing in production long after launch.
The failure mode for GenAI in production is rarely a bad build. It is abandonment. Model providers push new versions with changed behaviour. Prompt performance drifts as user inputs evolve in ways the original prompts did not anticipate. RAG retrieval quality degrades as your document corpus grows and existing embeddings go stale. Token costs shift as usage scales. A system that delivered reliably at launch routinely underperforms by month six, not because it was built wrong, but because nobody is maintaining it.
Project shops are not structured to solve this. They scope a build, deliver it, and move to the next client. The ongoing model operations work falls on your in-house team, which typically does not have the AI engineering depth to handle it without rehiring.
Scrums.com teams are permanent bench deployments that stay across model version cycles, prompt optimization rounds, and retrieval quality improvements. Our SEOP platform tracks hallucination rate, latency, and token cost as first-class delivery metrics from sprint one, so degradation surfaces in the data before it surfaces in user complaints. The engineers who built your system maintain it. Institutional knowledge stays with the engagement rather than walking out the door at handoff.
Deploying LLMs in regulated industries introduces risks that generic AI development partners do not plan for: data residency requirements, PII exposure in prompt context, model audit trails, and output governance. We plan for all of these from discovery, not from the compliance review that happens three months after launch.
Our generative AI development engagements include:
For FinTech, banking, and insurance clients, this is not optional compliance overhead. It is the difference between a GenAI feature that passes legal review and one that does not ship.
GenAI features that live outside your core product add complexity without delivering compounding value. We build LLM capabilities that integrate directly into your existing platforms, APIs, and workflows, so your users experience AI as a natural part of the product, not a separate tool they need to context-switch into.
Integration capabilities include:
All integrations are built to your existing SDLC practices, code standards, and deployment pipeline, not to a separate AI-specific process your team has to maintain separately.
Four phases. First production sprint in under 21 days.
Deliverable: Architecture decision record, data handling design, model selection rationale, and sprint plan.
Deliverable: Team operational, development environment live, first sprint kicked off.
Deliverable: Production-ready GenAI features shipped every two weeks.
Deliverable: Production GenAI system that improves with use, not one that decays after handoff.
Our AI engineers work across the leading LLM platforms, orchestration frameworks, and vector database technologies. We adapt to your existing infrastructure and select tools based on your performance, cost, and compliance requirements.
Generative AI development pricing varies based on team composition, model complexity, integration scope, and compliance requirements. The variables that move the number most:
These are not commitments, they give you a realistic frame for planning:
Permanent African AI engineering talent combined with AI-augmented delivery produces production-grade generative AI systems at 40 to 60 percent lower cost than US and UK firms, without compromising seniority, compliance posture, or delivery predictability. Subscription-based pricing, no scope-creep invoicing, no lock-ins.
View our pricing models or request a custom quote for your specific scope and requirements.
Generative AI in regulated industries is not the same as GenAI in consumer products. Compliance requirements, data sovereignty rules, and audit obligations change the architecture. Our engineers have shipped LLM-powered systems into FinTech, banking, and SaaS environments where these constraints are not optional.
Generative AI for clinical documentation, patient communication, and medical record summarization. We build HIPAA-compliant LLM systems for telehealth platforms, patient portals, and clinical decision support, including automated clinical note generation, discharge summary drafting, and AI-assisted triage, with the audit logging and data handling controls regulated healthcare environments require.
GenAI for shipment exception handling, carrier communication automation, and supply chain document processing. We build LLM-powered systems that extract structured data from unstructured logistics documents, draft exception communications, and answer operational queries against real-time fleet and inventory data, reducing manual processing in high-volume logistics operations.
Generative AI for product description generation, customer service automation, and merchandising intelligence. We build LLM systems that generate and optimize product catalog content at scale, power conversational commerce interfaces, and extract customer intent from support interactions to route and resolve queries without agent involvement.
Generative AI as a product feature: AI-powered search, in-app assistants, automated reporting, and code generation tools embedded in SaaS platforms. We help technology companies ship GenAI features that increase product stickiness, reduce support load, and create defensible differentiation, with model-agnostic architecture ensuring you can evolve as the landscape changes.
GenAI for network documentation automation, customer service deflection, and technical knowledge retrieval. We build LLM systems that surface network configuration knowledge, automate first-line customer issue diagnosis, and generate structured incident reports from unstructured engineer notes, reducing resolution time in high-volume telecom support operations.
Generative AI for policy document analysis, claims narrative processing, and underwriting support. We build LLM systems that extract structured data from unstructured claims submissions, generate compliance-aligned policy summaries, and surface relevant precedents for underwriting decisions. Built with GDPR, POPIA, and ISO 27001 data handling controls as standard.
GenAI for regulatory document analysis, AML narrative generation, and customer communication automation. We build LLM systems that process complex regulatory frameworks to surface relevant obligations, automate compliance narrative generation for SAR and AML reporting, and power secure banking chatbots with strict data residency and access controls aligned to SOC 2 and ISO 27001.
Generative AI for financial document intelligence, automated underwriting narratives, and customer-facing AI features in payments and lending platforms. We build LLM systems with PCI DSS-aligned data handling, fine-tuned models for financial language and reasoning, and RAG pipelines against proprietary financial datasets. Our engineers have shipped into FinTech production environments where compliance and uptime are non-negotiable.
Generative AI development is the engineering discipline of building products and systems powered by large language models and other generative AI technologies. This includes LLM integration, custom AI agent development, retrieval-augmented generation (RAG) systems, fine-tuning proprietary models, and deploying generative AI features into production applications. It is distinct from general AI automation (using AI to speed up internal SDLC workflows) and from standard software development where no generative model is involved.
Our AI and Automation Services deploy AI agents inside the software development lifecycle to automate QA testing, code review, deployment orchestration, and DevOps operations. The goal is faster, more reliable software delivery. Generative AI Development is different: it means building LLM-powered features and products into your own applications so your customers or employees can interact with AI. One improves how software is built; the other determines what the software does.
We are model-agnostic. Our engineers have production experience with OpenAI (GPT-4o, o1, o3), Google Gemini, Meta Llama (open-source, self-hosted), Mistral, Anthropic Claude, and Grok. Model selection is driven by your use case, latency requirements, cost targets, and data residency constraints, not by vendor agreements. Where appropriate we use multiple models in combination, routing tasks to the model best suited to each function.
Retrieval-augmented generation (RAG) is an architecture that connects an LLM to a searchable knowledge base of your own content, allowing the model to ground its responses in your proprietary data without exposing that data to third-party training. We recommend RAG when: you have a large corpus of internal documents, policies, or product content; you need responses that are specific and verifiable against a known source; or you cannot afford hallucinations in regulated or high-stakes outputs. RAG is often the right choice before fine-tuning, because it is faster to update and easier to audit.
Hallucination prevention is an engineering problem, not just a prompt engineering problem. Our approach combines: retrieval-augmented generation to ground responses in verified source documents; output validation pipelines that check responses against known facts or structured constraints; confidence scoring and fallback handling when the model is uncertain; human-in-the-loop review gates for high-stakes outputs; and continuous monitoring through SEOP that tracks output quality as a first-class delivery metric. No LLM system eliminates hallucinations entirely, but well-engineered systems contain them to acceptable rates for their use case.
Under 21 days from signed contract. We start from a permanent bench of senior AI engineers, not from a post-contract recruitment race. The 21-day window covers discovery, architecture design, environment setup, model selection, and first sprint kickoff. Focused integrations with clear scope can move faster. Complex enterprise systems with multiple data sources and compliance requirements take longer to architect but not longer to staff.
Yes. We have experience fine-tuning open-source models (Llama, Mistral) on client-specific datasets for domain adaptation, tone consistency, and task-specific accuracy. Fine-tuning requires sufficient labeled training data, a well-defined evaluation framework, and an MLOps pipeline for model versioning, deployment, and performance monitoring. We advise on whether fine-tuning or RAG is the better approach for your use case before committing to either.
Data handling architecture is designed before any code is written. For regulated clients, this typically means: keeping sensitive PII out of third-party model API calls by pre-processing or redacting inputs; deploying open-source models in self-hosted infrastructure where data sovereignty requires it; implementing role-based access controls on AI outputs; maintaining full audit logs of model inputs, outputs, and decisions; and aligning with SOC 2, GDPR, POPIA, and ISO 27001 where applicable. We do not treat compliance as a post-launch checklist item.
Production LLM systems require ongoing attention that traditional software does not. Model providers release new versions (sometimes with breaking changes), prompt performance drifts as user behavior evolves, RAG retrieval quality degrades as content grows stale, and token costs change as usage scales. Our ongoing maintenance includes: model version evaluation and migration planning, prompt optimization based on production usage patterns, RAG corpus refresh and retrieval quality monitoring, cost optimization as scale increases, and performance benchmarking against your quality thresholds. This is what keeps a GenAI feature performing at launch quality twelve months later.
Deploy generative AI development teams in under 21 days. Model-agnostic expertise across OpenAI, Gemini, Llama, Mistral, Anthropic, and Grok. 40 to 60 percent cost savings versus US and UK firms. Compliance-safe architecture from sprint one. No lock-ins.
OPERATED DELIVERY · 5 REGIONS · FIRST SPRINT IN 21 DAYS