Services
Generative AI for the enterprise
A demo works on ten documents. A production assistant has to respect permissions, say when it does not know and be measured. We build for the second case.
What organizations bring us
Generative AI projects tend to stall at the same places. Retrieval returns the wrong passages. The model answers confidently from nothing. Users see documents they should not. Nobody can say whether version two is better than version one.
The engineering answer is evaluation data, retrieval quality measures, permission-aware search, human approval for consequential actions and logging. These are unglamorous and they decide whether the system survives contact with real users.
What the platform covers
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Enterprise copilots
Assistants embedded in the tools people already use, scoped to a role and a task.
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Retrieval-augmented generation
Answers grounded in your documents, with citations to the source passage.
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Knowledge assistants
Search and Q&A over policies, procedures, tickets and technical documentation.
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AI document processing
Extraction and classification from contracts, invoices and forms, with confidence scores and review queues.
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Workflow automation
Models inside business processes for triage, drafting and routing, with a human in the loop.
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Agent-based workflows
Systems that plan and call tools, with scoped permissions and approval steps.
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Application integration
Calling models from your applications through APIs, with caching, fallbacks and cost controls.
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Model deployment
Hosted APIs, private endpoints or self-hosted models, chosen by data sensitivity and cost.
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Governance and security
Policy, access, logging, red teaming and incident handling for AI systems.
A retrieval-based assistant, layer by layer
The model is one component. Most of the quality comes from the layers around it.
Sources and ingestion
- Document stores
- Wikis
- Ticketing
- Databases
Parsing, cleaning, chunking and metadata. Access control lists captured with the content.
Embeddings and index
- Vector embeddings
- Keyword index
- Hybrid search
Vector search finds meaning; keyword search finds exact terms. Hybrid usually beats either one.
Retrieval
- Query rewriting
- Semantic retrieval
- Re-ranking
- Permission filter
Filter by the asking user’s permissions before the model sees anything.
Generation
- Model selection
- Prompt and context engineering
- Structured output
Instruct the model to answer only from the passages and cite them.
Controls
- Hallucination checks
- Prompt injection protection
- Output filtering
- Human approval
Refuse or escalate when retrieval is weak. Approve consequential actions.
Evaluation and operations
- Evaluation data sets
- Retrieval quality metrics
- Logging and audit
- Cost and latency monitoring
Every change is tested against the same questions.
Common use cases
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Policy and procedure assistant
Employees ask questions; answers cite the current policy version and the source page.
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Support agent assist
Suggested replies and relevant knowledge articles, reviewed by an agent before sending.
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Contract and document review
Extraction of clauses and dates, with highlights for a human to confirm.
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IT service desk triage
Classify and route tickets, propose known fixes and escalate when unsure.
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Engineering knowledge search
Questions across runbooks, architecture records and incident write-ups.
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Report drafting
First drafts from structured data, edited and approved by the owner.
How we build one
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Define the task
Who asks what, what a good answer is, and what must never happen.
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Build an evaluation set
Real questions with reference answers, written with the people who know them.
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Prototype retrieval first
Measure whether the right passages are found before tuning prompts.
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Add controls
Permissions, refusals, citations, logging and approvals.
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Pilot with real users
A small group, with feedback and review of failures.
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Monitor
Quality, cost, latency, abuse and drift, with an owner.
An evaluation loop, illustrated
Illustrative scenario. Not a real client engagement. No outcomes are claimed.
A made-up example of how evaluation changes decisions. No client or results are implied.
- Baseline
- A policy assistant is tested on 120 questions written by HR and finance staff. Reviewers mark answers correct, partly correct, wrong or refused.
- Diagnosis
- Most wrong answers trace to retrieval, not the model: the right paragraph was split across two chunks.
- Change
- The team changes chunking, adds a re-ranker and reruns the same questions.
- Decision
- The change ships only if the correct-answer rate improves and the number of confident wrong answers does not rise.
What you receive
- Use case definition and risk assessment
- Evaluation data set and scoring method
- Retrieval and prompt design
- Permission model for content
- Guardrail and approval design
- Monitoring dashboard
- Operating runbook
Consultants we place
Staffing is a separate engagement from project delivery. You can use either, or both.
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AI engineer
Builds retrieval and agent systems.
- Python
- APIs
- vector search
- evaluation
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ML engineer
Deploys and monitors models.
- MLOps
- serving
- cost and latency
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Prompt and context engineer
Designs prompts, context and structured output.
- Evaluation design
- domain knowledge
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AI governance lead
Sets policy and review processes.
- Risk assessment
- documentation
- compliance
Related technology
Listed as technologies we work with. This is not a statement of vendor partnership, certification or reseller status. Model providers are chosen per use case and data sensitivity.
Questions people ask
Will the model train on our data?
That depends on the provider and contract. We choose deployment options to match your data policy and tell you what each one means.
How do you handle hallucinations?
By grounding answers in retrieved passages, requiring citations, refusing when retrieval is weak and measuring wrong answers on an evaluation set. They are reduced, not eliminated.
Are agents safe to use?
They can be, with scoped tool permissions, approval for consequential actions and logs. Our AI security services test those limits.
Do you test AI systems for security?
Yes. See LLM security assessments, AI red teaming and MCP security assessments.
Related
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LLM security assessments
Test chatbots, copilots and LLM-backed features for prompt injection, data leakage, unsafe output handling and excessive permissions.
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RAG security
Review retrieval-augmented generation: document-level authorization, poisoning, embedding exposure and leakage through answers.
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AI opportunity assessment
Answer nine questions about a process and get an illustrative, phased roadmap with the risks, data and security steps. No ROI figures.
Have a technology challenge? Let’s work out the approach.
Discuss a project
Tell us the current system, the target and the timeline. A technical lead replies to talk about scope.
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