Generative AI and large language model solutions for drafting, summarising and answering, built around your own content and rules.
About Generative AI & LLM Solutions
Generative AI uses large language models to write, summarise, translate, classify and answer questions in plain language. Applied well, it removes hours of repetitive reading and writing from knowledge work.
The model alone is rarely the answer. Useful solutions connect the model to your documents and systems, set clear limits on what it may say or do, and keep a person in charge of decisions. That is where design effort goes.
What is included in Generative AI & LLM Solutions
The typical building blocks. Scope is agreed with you in writing and tailored to your situation.
Use case design
Defining the task, the inputs, the expected output and how quality will be judged.
Prompt and workflow design
Instructions, examples and structure that make outputs consistent.
Connection to your content
Grounding answers in approved documents and data instead of guesses.
Guardrails
Rules for tone, scope, sensitive topics and when to hand over to a person.
Evaluation set
A set of real examples used to measure quality before and after changes.
Integration and rollout
Connecting to the tools your team already uses, with training for the people who will use it.
Why teams choose Generative AI & LLM Solutions
Time back for your team
Routine drafting, summarising and triage take minutes instead of hours.
Consistent output
Tone, format and policy stay the same across people and shifts.
Control over data
You decide what content the system can see and what it must never do.
This is a good fit if
- Your team spends a lot of time reading, writing or searching documents.
- You want an assistant that follows your policies.
- You need to show how answers were produced.
Does this sound familiar?
Pilots that never reach production
Answers that are not grounded in company knowledge
No clear policy for data, risk and human approval
Works well with
Other AI & Intelligence services that often come with Generative AI & LLM Solutions.
How it fits together
A typical reference architecture, from your data to the people who use it.
- Layer 1Your data and documents
- Layer 2Retrieval and guardrails
- Layer 3Model and approved tools
- Layer 4Apps, CRM and channels
Technology we typically work with
Six stages, the same every time
You always know which stage you are in and what comes next.
- Stage 1
Discover
Workshops and stakeholder interviews to define goals and constraints.
- Stage 2
Assess
Current-state review of systems, data, security and spend.
- Stage 3
Architect
Target design, integration map and phased plan with owners.
- Stage 4
Implement
Build in short releases with demos, tests and staging sign-off.
- Stage 5
Optimize
Measure against agreed KPIs and tune performance and cost.
- Stage 6
Manage
Monitoring, support and a monthly review with an account manager.
Security and governance
How the work is kept safe and accountable.
- Per-tenant retrieval and tool allowlists
- Prompt-injection defences and rate limits
- Human approval for commercial or operational actions
Industries we have worked in
Ways to work together
Scope and price are confirmed in writing by a consultant after discovery.
Assessment
A fixed-scope review that ends in a written roadmap you can keep, with or without us.
- Current-state review
- Ranked priorities
- Written roadmap
Project
Defined scope, milestones and acceptance criteria for building or migrating something specific.
- Phased plan with owners
- Short releases and demos
- Handover and training
Managed
A monthly retainer once it is live: monitoring, support and a regular review.
- Monitoring and backups
- Named account manager
- Monthly review
What clients say about working with us
We asked for help with Generative AI & LLM Solutions and expected a sales pitch. They told us when something was not a good fit for us. We always knew where things stood.
Our Generative AI & LLM Solutions project had stalled before we called Bedre. The plan was clear enough to share with our board. It was a calm, well-organised project.
Our team had tried to sort out Generative AI & LLM Solutions alone. They explained the options in plain words and let us decide. We always knew where things stood.
We needed Generative AI & LLM Solutions and did not have the time to figure it out ourselves. The work was split into small steps we could review. It felt like working with our own team.
Our team had tried to sort out Generative AI & LLM Solutions alone. Communication was steady and honest throughout. It was a calm, well-organised project.
We came in unsure where to start with Generative AI & LLM Solutions. They explained the options in plain words and let us decide. The documents they left behind are still useful.
That depends on the service and settings chosen. Data handling is agreed up front, and the design avoids sending more data than a task needs.
Yes. Language models can be wrong. Good design limits answers to approved sources, shows where they came from and keeps a person reviewing anything important.
The model is chosen per use case based on quality, cost, speed and data requirements, and can be changed later.
It depends on scope. The Assess stage produces a timeline you can review before committing.
You do. Domains, cloud accounts and data stay in your name wherever possible.
Usually yes. The Architect stage lists connectors and any limits.
Ready to scope Generative AI & LLM Solutions?
Tell us the outcome you want. We will bring the plan.