RAG copilots
Retrieval-augmented copilots grounded in your content, with prompt management, evaluation, and red-teaming built in.
ExpertiseConsulting, engineering and managed services
AI Orchestration
Production-grade AI on Azure, with copilots, agents, and document intelligence your teams can actually trust.
7expertise practices
3public clouds: Azure, AWS, GCP
150+professional certifications
01 / Overview
What we do
At a glance
Core platforms
Azure OpenAI, Claude, AI Search
Typical first win
Governed copilot in ~4 weeks
Governance
ISO 42001, content safety, audit logs
Engagement
Co-managed or fully managed
We help enterprises move from AI experiments to governed, measurable production – grounded in your own data, evaluated before release, and instrumented for cost.
We're model-agnostic: Azure OpenAI where it fits, open or third-party models where they're better, always behind the same safety, evaluation, and audit layer.
How we engage
01
Discover
Scoped, costed assessment before any build.
02
Design
Reference architecture and delivery plan.
03
Deliver
Senior engineers build in evidenced increments.
04
Operate
24x7 managed operations with named owners.
Held · re-earned annually
This expertise is backed by a Microsoft competency audited against real delivery - not a self-declared skill.
About this designation →02 / Capabilities
What's inside the practice
Each capability is a sub-service we scope, deliver, and operate, with the engineering specifics, not brochure lines.
Agents & automation
Multi-step agents that act across Microsoft 365 and line-of-business systems, with human-in-the-loop guardrails.
Document intelligence
Extract, classify, and summarize unstructured documents at scale with hybrid OCR and LLM pipelines.
Responsible AI
Content safety, PII redaction, audit logging, and an AI governance framework aligned to ISO/IEC 42001.
03 / Reference architecture
How we build it
A reference pattern, not a template. Every layer is tailored to your environment, constraints, and compliance posture.
Experience
Copilot UI, Teams / M365 apps, API & webhooks
Orchestration
Prompt flow, Agents & tools, Guardrails
Models
Azure OpenAI, OpenAI / Claude, Open models
Knowledge
Vector index, AI Search, Doc Intelligence
Foundation
Landing zone, Private endpoints, Key Vault
Cross-cutting
Identity (Entra)
Content safety
Evaluation & logging
Cost controls
04 / Technology
Vendor-agnostic by design
We hold deep Microsoft specializations, and we are deliberately multi-vendor. We pick the platform and tooling that fit your outcome, your team, and your constraints, never a single badge.
The stack below is representative; we work with what you already run, and tell you plainly when something should change.
Models
Azure OpenAI, OpenAI, Anthropic Claude, Hugging Face, Mistral
Orchestration
Prompt flow, LangChain, Semantic Kernel, Copilot Studio, N8N
Knowledge & vectors
Azure AI Search, Pinecone, pgvector, Document Intelligence
MLOps & governance
Azure AI Foundry, MLflow, Content Safety, Azure Monitor
05 / Engineering standards
We go deep, on purpose
Whatever the practice, the same engineering discipline holds. These are the commitments behind every BITSUMMIT delivery.
Infrastructure as code
Every environment is reproducible: Bicep, Terraform, and Git, never console clicks.
Observability by default
Dashboards, alerts, and SLOs wired in before go-live, not after the first incident.
Security baselines
CIS and Microsoft baselines applied as policy, with drift detection and remediation.
Tested recovery
Backups and failover are proven on a schedule, with named owners and runbooks.
Documented and yours
Architecture decision records and runbooks you own: no black boxes, no lock-in.
FinOps discipline
Cost is a first-class metric: budgets, tagging, and right-sizing from day one.
Standards & frameworks
Built to the standards your auditors check
We build to the standards your auditors, insurers, and regulators actually check – and keep the evidence current between audits.
ISO/IEC 42001 alignment
An AI governance framework aligned to ISO/IEC 42001 - content safety, PII redaction, and audit logging built into every deployment.
Evaluated before release
Copilots and agents ship with evaluation harnesses and red-teaming, grounded in your own data - nothing reaches production on a demo alone.
Fully auditable
100% of AI interactions are logged and evaluable for audit, with human-in-the-loop guardrails on agent actions.
Cost instrumented
Token usage is instrumented per workload with budgets and alerts, and models are right-sized so simple tasks route to cheaper deployments.
06 / By the numbers
Outcomes our clients see
60
%
Reduction in document handling time after deployment.
4
wk
From discovery to a governed copilot in production.
100
%
Of AI interactions logged and evaluable for audit.
Related expertise
Explore adjacent practices
07 / Case study · Financial Services
Driving 90% Copilot Adoption in Three Months
BITSUMMIT's change management drove 90% Microsoft Copilot adoption at Money Mart in three months - 35% faster reporting and a 20% lift in employee satisfaction.
90
%
User adoption within three months
35
%
Faster reporting, analysis & document prep
20
%
Increase in employee satisfaction
08 / Insights
From the BITSUMMIT blog
09 / FAQ
Questions we hear
What does AI governance actually mean here?
A framework aligned to ISO/IEC 42001 - content safety, PII redaction, and audit logging built into every deployment, not bolted on after.
How do you keep agents safe?
Multi-step agents run with human-in-the-loop guardrails, and 100% of AI interactions are logged and evaluable for audit.
Are you tied to one model vendor?
No. We're model-agnostic - Azure OpenAI where it fits, open or third-party models where they're better, always behind the same safety, evaluation, and audit layer.
How fast can we get to production?
A governed copilot typically reaches production in about four weeks - discovery, grounding in your data, evaluation, then release.
How do you control AI cost?
We instrument token usage per workload, set budgets and alerts, and right-size models, routing simple tasks to cheaper deployments.
Does our data train the model?
No. Azure OpenAI keeps your prompts and data within your tenant boundary – nothing is used to train foundation models.
Let's break some barriers.
Tell us what you're trying to modernize, secure, or migrate. We'll bring a plan and a named senior engineer, not a sales pitch.
Schedule a call →
A named senior engineer will respond within one business day.
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