Generative AI Development Services for Intelligent Business Applications

AI Knowledge Assistants

Internal AI knowledge assistants let employees query organizational knowledge in natural language, rather than searching through static documentation or asking colleagues directly:

• Company documentation — policies, procedures, and internal reference material
• Policies and SOPs — standard operating procedures employees can query directly
• Product manuals — technical or product documentation surfaced through natural language questions
• FAQs — commonly asked internal questions answered automatically and consistently
• Internal knowledge — institutional knowledge that would otherwise live only in specific people's heads
• Structured databases — connecting natural language queries to structured internal data sources

Technical and governance considerations:

• RAG — the underlying technique making the assistant's answers grounded in actual company content
• Access control — ensuring the assistant only surfaces content a given user is permitted to see
• Citations — showing users which source documents informed a given answer
• Permissions — respecting existing organizational role-based access
• Data security — protecting sensitive internal content throughout the retrieval and generation pipeline
• User roles — potentially tailoring available content or capability based on a user's role

Enterprise knowledge assistants require more careful access-control engineering than a typical consumer-facing RAG application, since internal content often includes material that shouldn't be uniformly accessible to every employee.

AI Document Intelligence

Generative AI-powered document processing extends beyond traditional OCR and extraction by adding language understanding to how documents are processed:

• Contracts — extracting key terms, obligations, and dates from legal agreements
• Invoices — automatically extracting line items, amounts, and vendor information
• Reports — summarizing lengthy reports into digestible key points
• Forms — extracting structured data from submitted forms
• Applications — processing structured application data for review workflows
• Proposals — summarizing or extracting key details from business proposals
• Technical documents — making dense technical content searchable and summarizable
• Business records — extracting and structuring data from varied business document formats

Technical approach:

• OCR — converting scanned or image-based documents into machine-readable text where needed
• Extraction — pulling specific structured fields out of document content
• Classification — automatically categorizing incoming documents by type
• Summarization — condensing long documents into concise summaries
• Structured output — returning extracted data in a consistent, predictable format
• Validation — checking extracted data against expected formats or business rules
• Human review — building in review steps for high-stakes documents

We do not make legal or medical accuracy guarantees for document intelligence systems — any application processing legal, medical, or otherwise high-stakes documents should include appropriate human review.

Vector Databases & Embeddings

What are embeddings?

Embeddings are numerical representations of text (or other content) that capture semantic meaning as coordinates in a high-dimensional space, such that content with similar meaning ends up positioned close together, regardless of exact wording.

What are vector databases?

Vector databases are specialized database systems optimized for storing embeddings and performing fast similarity search across potentially millions of vectors — a task traditional relational databases aren't optimized for.

Why they're useful:

• RAG — vector databases are the retrieval infrastructure that makes finding relevant source content fast and scalable
• Semantic search — the same underlying infrastructure powers meaning-based search
• Recommendations — finding similar items based on embedding similarity
• Knowledge assistants — the retrieval backbone for grounding assistant responses in organizational content
• Document retrieval — efficiently searching large document collections by meaning

Examples of vector database technologies we may work with depending on project requirements: Pinecone, Weaviate, Qdrant, Milvus, and PostgreSQL with the pgvector extension. We select the specific vector database based on project scale, existing infrastructure, and operational requirements — we don't claim formal partnerships with any of these vendors.

Prompt Engineering

Prompt engineering is the deliberate, iterative design of the instructions and context given to an LLM to produce reliable, useful output:

• System prompts — the foundational instructions establishing an LLM's role, constraints, and behavior
• Instruction design — crafting clear, specific instructions that reduce ambiguity
• Structured outputs — designing prompts that reliably produce output in a specific, parseable format
• Few-shot examples — including example inputs and desired outputs within a prompt
• Prompt templates — reusable, parameterized prompt structures that maintain consistency
• Context management — deciding what information to include in a given prompt
• Tool instructions — for agentic systems, clearly specifying when and how the model should use available tools
• Evaluation — systematically testing how prompt changes affect output quality

Production generative AI systems require more than a single prompt. A single, static prompt rarely holds up across the full range of real user inputs a production system encounters. Reliable systems typically involve iterative prompt refinement based on evaluation results, prompt templates that adapt to different contexts, and ongoing adjustment as usage patterns reveal edge cases.

LLM Model Strategy

• Commercial APIs — accessing LLM capability through a vendor's hosted API
• Open-source models — models with publicly available weights that can be run independently
• Hosted models — models run on managed infrastructure you don't operate directly
• Self-hosted models — models deployed and run on infrastructure you control directly
• Fine-tuned models — models further trained on your specific data to adapt behavior or task performance

Selection depends on:

• Cost — commercial APIs typically have usage-based pricing; self-hosting shifts cost toward infrastructure
• Latency — self-hosted models can offer more predictable latency
• Privacy — self-hosted or open-source models keep data within your infrastructure
• Performance — different models have different capability levels for a given task
• Context requirements — how much context a use case needs to process at once
• Customization — fine-tuning offers deeper behavioral customization than prompting alone
• Infrastructure — self-hosted and fine-tuned models require infrastructure and expertise

No single model is always best. The right choice depends on your specific use case's cost sensitivity, latency requirements, data privacy needs, and performance requirements — we evaluate this during discovery rather than defaulting to one provider or approach.

Generative AI Automation

Generative AI can automate content-heavy and language-intensive workflows that traditional automation struggles with:

• Document workflows — processing, classifying, and extracting data from incoming documents
• Email processing — classifying, summarizing, or drafting responses to incoming email
• Content workflows — assisting with drafting, editing, or reviewing content at various stages
• Customer support — handling common inquiries and drafting responses for agent review
• Data extraction — pulling structured information from unstructured text sources
• Research — gathering and synthesizing information from multiple sources toward a specific question
• Reporting — generating draft reports or summaries from underlying data
• Internal knowledge retrieval — helping employees find answers without manual searching
• Workflow routing — using language understanding to direct incoming requests to the right process

Human approval and validation matter for sensitive workflows. For automation touching financial transactions, legal commitments, customer communications with real consequences, or any other high-stakes decision, we build in human review checkpoints rather than fully automating the decision.

Enterprise Generative AI

Enterprise generative AI deployment involves distinct governance and technical requirements beyond a smaller-scale application:

• Internal AI assistants — knowledge tools respecting existing organizational access controls
• Enterprise search — search across large, often siloed knowledge bases spanning multiple systems
• Knowledge systems — centralized, governed access to organizational knowledge
• AI copilots — embedded assistance across multiple internal tools and workflows
• Workflow automation — automating processes that span departments and existing systems
• Document intelligence — high-volume document processing integrated with existing document management systems
• AI analytics — natural language interfaces to enterprise data and reporting
• Enterprise integrations — connecting generative AI capability to the broader set of systems typical of enterprise environments

Enterprise-specific requirements:

• Access control — respecting existing organizational permission structures throughout retrieval and generation
• Security — meeting enterprise security expectations, often stricter than smaller-scale deployments
• Privacy — careful handling of sensitive data referenced in prompts, retrieved content, and generated outputs
• Monitoring — production-grade visibility into system usage, cost, and output quality
• Scalability — architecture that handles enterprise-scale usage volume reliably
• Governance — clear ownership, policies, and oversight for how generative AI is deployed and used
• Auditability — traceable records supporting accountability, particularly important for regulated industries
• Model evaluation — ongoing, structured assessment of output quality at enterprise scale

Enterprise generative AI projects typically require more extensive discovery, stakeholder alignment, and governance planning than smaller deployments, given the number of systems, data sources, and user groups typically involved.

Generative AI Security

Generative AI systems require security attention beyond standard application security practices:

• Authentication and authorization — controlling who can access generative AI capabilities and what data they can reach
• Encryption — protecting sensitive data in transit and at rest
• API security — authenticated, rate-limited access to LLM and application endpoints
• Data isolation — ensuring one user's or tenant's data doesn't leak into another's context or responses
• Access controls — restricting what data and functions a generative AI system can reach
• Secure model access — controlling how the application connects to and authenticates with LLM providers
• Sensitive information handling — particular care around personally identifiable or confidential information
• Logging and monitoring — tracking system usage and behavior for accountability and anomaly detection

Generative AI-specific threats:

• Prompt injection — malicious input designed to manipulate an LLM into ignoring its instructions
• Data leakage — sensitive information inadvertently appearing in generated output
• Malicious inputs — adversarial input designed to extract unintended information or behavior
• Insecure tool calls — for agentic systems, ensuring tool/function calls can't be manipulated into unauthorized actions
• Hallucinations — generated content not actually grounded in fact, with security implications when acted on without review
• Unauthorized data retrieval — RAG systems retrieving and surfacing content a given user shouldn't have access to
• Model misuse — using the system for purposes beyond its intended, sanctioned use

Guardrails and validation address these risks through input validation, output validation, scoped tool permissions, and access-control-aware retrieval.

We do not claim compliance with HIPAA, SOC 2, ISO 27001, GDPR, or other regulatory frameworks unless specifically verified for a given engagement.

AI Guardrails

Guardrails are the constraints and validation mechanisms that keep a generative AI system's behavior within appropriate bounds:

• Input validation — screening and constraining what reaches the model
• Output validation — checking generated content against expected formats, appropriateness, and factual grounding
• Moderation — filtering generated content for inappropriate, unsafe, or off-policy output
• Structured outputs — constraining the model to produce output in specific, predictable formats
• Access controls — limiting what data and functions the system can actually reach
• Tool restrictions — for agentic systems, limiting which tools an agent can call and under what conditions
• Human approval — requiring human review before consequential actions execute
• Monitoring — ongoing visibility into system behavior in production
• Evaluation — systematic testing of whether guardrails are actually functioning as intended

LLMs are probabilistic systems that don't guarantee a specific output for a given input, and can occasionally be manipulated through crafted adversarial input. Guardrails meaningfully reduce the practical risk and impact of unreliable or manipulated output reaching users or triggering unintended actions.

LLM Evaluation

Evaluating whether a generative AI application actually works well requires more structure than informal impression-based testing:

• Accuracy — whether generated content is factually correct
• Relevance — whether responses actually address what the user asked
• Groundedness — for RAG systems specifically, whether the response is actually supported by the retrieved context
• Hallucination rate — how often the system generates content not actually supported by available information
• Retrieval quality — for RAG systems, whether the retrieval step is finding genuinely relevant content
• Response consistency — whether the system produces reasonably consistent output for similar inputs
• Latency — how quickly the system responds
• Cost — the per-request or per-usage cost of running the system at production volume
• Safety — whether the system avoids generating harmful, inappropriate, or policy-violating content

Evaluation approach:

• Test datasets — representative sets of realistic inputs used to systematically test system behavior
• Evaluation criteria — clearly defined standards for what counts as a good or acceptable response
• Human evaluation — human review of system outputs, particularly important for nuanced quality judgments
• Automated evaluation — programmatic checks for specific quality dimensions
• Continuous monitoring — ongoing evaluation in production, not just at launch

We don't promise specific accuracy percentages for generative AI systems — actual output quality depends heavily on the specific use case, underlying data quality, and how well the system is scoped and evaluated.

Generative AI Use Cases by Industry

Illustrative use cases based on common generative AI application patterns — not claims of completed projects.

Technology Stack

Technology selection is scoped to what a specific generative AI project actually requires.