Evaluation before launch
We build a test set from your real questions and score every change against it. Without that, 'it seems better' is the only quality signal you have, and it is not one.
LLM systems that hold up outside the demo
Retrieval, assistants, agents and document workflows built on your own content, with evaluation and guardrails in place before anyone in the business relies on them. Most of the work is retrieval quality, evaluation and cost control — the prompt is the easy part.
Or see how we engage first — four shapes, no sales call required.
- RAG
- Assistants & copilots
- Agents & tool use
- Document extraction
- Semantic search
- Summarisation
- Structured output
- Evaluation harnesses
- Guardrails
- Prompt versioning
- Vector databases
- Embeddings
- Fine-tuning
- Token cost control
- Claude & OpenAI APIs
- Self-hosted models
No more demos that break on the second real question
Forget answers with no source anyone can check
Stop shipping prompt changes nobody evaluated
Skip the bill nobody forecast before launch
Done with agents that take actions you cannot audit
Say bye to a chatbot the team stops using after a fortnight
80/20
of RAG quality comes from retrieval and chunking, not the modelindustry context
10–100×
cost spread between a naive and a tuned implementation of the same featureindustry context
0
un-evaluated prompts we will put in front of your customersour rule
Why choose Backendz for Generative AI?
Retrieval is the product
Chunking, metadata, hybrid search and re-ranking get the attention. A strong model over weak retrieval gives you a confident wrong answer, which is the worst outcome available.
Cost modelled, not discovered
Token cost per interaction is estimated during design and measured in staging. You see the monthly run rate before launch, with the levers that move it.
Model-portable by default
We keep the provider behind an interface. Switching between Claude, OpenAI or a self-hosted model is a configuration change, not a rewrite.
We build Generative AI
Four ways this usually starts
Most enquiries land in one of these. If yours does not, say so on the call — we would rather shape the engagement than sell you the nearest shape we have.
You want to find the real use case
A short discovery that ends with one or two use cases worth building and a list of the ones that are not, tested against your actual content.
- Use-case workshop with the people doing the work
- Content and permissions audit
- Throwaway prototype on real documents
- Cost and feasibility written down
Best for: A mandate to 'do something with AI' and no chosen target.
Most teams start here
You need answers over your own content
Retrieval-augmented generation over your documents, wikis, tickets or catalogue — with citations, permissions and an evaluation harness.
- Ingestion and chunking tuned to your content
- Hybrid search with re-ranking
- Citations back to the source document
- Permission-aware retrieval per user
Best for: Support, sales enablement, policy, technical documentation.
You need work done, not answers
Agents and pipelines that extract, classify, draft and route — with a human in the loop wherever the action is irreversible.
- Structured extraction with schema validation
- Tool use against your own APIs
- Human approval on consequential steps
- Full audit trail of every action taken
Best for: Document-heavy back-office processes.
You are putting AI in your product
A customer-facing feature with the operational scaffolding a product needs: limits, abuse handling, latency budgets and per-tenant cost.
- Streaming responses and latency budgets
- Rate limiting, quotas and abuse handling
- Per-tenant usage and cost attribution
- Offline evaluation plus live feedback capture
Best for: Software companies adding AI to an existing product.
Ideas that became systems in production
Every card is a build we shipped and still support. Open one for the decisions behind it.
We serve the best in the industry
All of it, written down
What falls inside this service. Nothing here is an upsell discovered later.
What we build
- Retrieval-augmented question answering
- Internal assistants and copilots
- Document extraction and classification
- Summarisation and report drafting
- Semantic and hybrid search
- Agents with tool use
- Content generation pipelines
- Natural-language interfaces to your data
Making it trustworthy
- Evaluation sets from your real questions
- Regression testing on every prompt change
- Citations and source linking
- Refusal and fallback behaviour
- Prompt-injection hardening
- Human-in-the-loop on irreversible actions
Running it
- Provider abstraction and failover
- Caching and token cost control
- Streaming and latency tuning
- Usage, cost and quality dashboards
- Feedback capture from real users
- Self-hosted options where data cannot leave
Stack
- Claude, OpenAI, open-weight models
- pgvector, Qdrant, Pinecone, OpenSearch
- LangChain, LlamaIndex or plain SDKs
- Python and TypeScript
- AWS Bedrock, Azure OpenAI, Vertex AI
- vLLM and Ollama for self-hosting
How it works?
The sequence matters more than the label. Skipping the first one is how projects in this area go wrong.
We look for a task with a clear right answer, enough volume to matter, and content good enough to ground it. Anything else waits.
From the people who use it daily
Operations directors, CTOs and plant managers — the ones still using the system a year after the invoice cleared.
Freight operator · 14 depotsFreight operator · 14 depotsWe wanted answers our team could check against the original document. Having the relevant clause beside the response makes the assistant useful when reviewing policies and contracts.
Our rental journey had to work for both vehicle owners and travellers. Backendz brought listings, availability and bookings together into a platform that feels straightforward to use.
A single headcount does not tell us where congestion is developing. The zone-based view gives our team a clearer picture of how people are moving and where attention may be needed.
Tender preparation involves gathering evidence and getting the right people to review each section. Backendz brought those steps into one workspace, with drafts our team can check and approve.
Our estimating process needed to reflect how we actually price work. Backendz took time to understand the inputs and rules, then built a quoting journey our team could review and refine.
The team took time to understand how our staff work between appointments. The resulting system brings scheduling and client information together without making the day more complicated.
We could review working features throughout the build and give specific feedback. That made it easier to keep the product focused on the people using it.
Our field team needed access to job details away from the office. The mobile workflow keeps the relevant information close at hand and gives the office a clearer view of progress.
We wanted a client portal that reflected how we deliver our services. Backendz brought documents, requests and progress updates into a straightforward experience for clients and staff.
Our teams were entering the same information into several tools. Backendz mapped the process with us and built a more connected workflow with clear review points.
We needed scheduling, availability and shift changes in one place. Backendz shaped the system around the way our operations team works and stayed involved after launch to help us refine it.
Ask for references and we will put you on a call with the team that runs the system — not a case study PDF.
References available on request
We wanted a practical way to review PPE and safety-zone events using our existing cameras. The team focused on the situations our site managers need to investigate and made the footage easier to review.
The priority was a counting workflow our warehouse team could use during a normal shift. Backendz connected image-based counts with the records we already use and made discrepancies easier to review.
Our information was spread across several operational systems. Backendz designed a clearer way to ask questions, inspect the supporting evidence and keep approval with our team before an action is taken.
The reconciliation workflow gives our finance team one place to review mismatches and supporting records. We can trace an exception back to its source and decide what needs attention.
We had outgrown the spreadsheets that held our pricing rules. Backendz helped turn that knowledge into a quoting tool we can maintain and explain to new colleagues.
Connecting our sales and inventory systems was the priority. The integration gives our team a clearer view of updates and a practical way to investigate records that need review.
The cloud work started with understanding our existing systems and dependencies. The team documented the setup and gave us a clearer basis for monitoring and ongoing maintenance.
The engineers worked alongside our team and explained their decisions clearly. Code reviews and practical documentation helped us stay involved throughout delivery.
Why Backendz
5 yrs
In production
Ops CopilotTender Response DrafterPolicy & Contract AssistantPallet CountingConstruction Site PPE & Safety MonitoringCrowd Density MonitoringDigital Security PlannerVirtual EstimatorJoblogyPamacraftsRollBnBVenus AestheticsShiftTrackerFreight DispatchInvoice Reconciliation AIChilled TraceabilitySteel Quoting EngineFactory Floor AppFleet Driver App
We quote fixed and we hold it
2
Platforms run in-house
RAGAssistants & copilotsAgents & tool useDocument extractionSemantic searchSummarisationStructured outputEvaluation harnesses
Cloud Strategy & ArchitectureCloud MigrationCustom ERP DevelopmentInfrastructure ModernisationCloud EngineeringERP ImplementationCloudOpsERP IntegrationDevOpsSupport & Managed Cloud ServicesERP Customisation & ModulesCloud Cost OptimisationERP Data MigrationCloud Security & ComplianceAI & Edge InfrastructureERP Support & OptimisationSaaS Stack AuditCustom CRM DevelopmentCustom HR & Workforce ManagementCustom Job & Field Service ManagementCustom Client Portals & BookingSaaS Data Migration & ExitAI Proof of Concept & MVP
GuardrailsPrompt versioningVector databasesEmbeddingsFine-tuningToken cost controlClaude & OpenAI APIsSelf-hosted models
AI Agent DevelopmentCustom AI Assistants & ChatbotsGenerative AIAI Workflow AutomationNatural language processingCustom Software DevelopmentComputer Vision DevelopmentWeb App DevelopmentMobile App DevelopmentAI Video Analytics for CCTVProduct DevelopmentMVP DevelopmentUX/UI DesignApplication ModernisationSoftware Testing & QAMachine Learning & Predictive AnalyticsERP & operationsAI Product DevelopmentData & cloudProduct & designAutomation & integrationsSupport & operations
45
Services, one team
Senior engineers, not account managers
24/7 production support and monitoring
Still running on workarounds?
FAQ
The things people ask before they commit.
Still have questions?
Ask it on the call. We would rather answer an awkward question early than discover it in month three.
Book a callDoes our data get used to train someone else's model?
Not on the enterprise API tiers we deploy against, and we configure it explicitly. Where the contract or the regulator will not allow data to leave your estate at all, we self-host an open-weight model instead and tell you plainly what that costs in quality.
How do you stop it making things up?
Ground every answer in retrieved content, cite the source, and make refusal a first-class behaviour — a system that says 'not in the documents' is more useful than one that guesses. Then we measure it, with an evaluation set built from your own questions, so hallucination rate is a number you can watch rather than an anecdote.
What will it cost to run?
We estimate token cost per interaction during design and measure it in staging, so you have a monthly run rate before launch. Caching, smaller models for easy paths and tighter retrieval are the usual levers, and they routinely move the bill by an order of magnitude.
Which model should we use?
Whichever wins on your evaluation set at an acceptable cost and latency. We keep the provider behind an interface so that answer can change without a rewrite, because it will — the frontier moves every few months.
Can it act on our systems, not just answer?
Yes, through tool use against your own APIs. Anything irreversible goes behind human approval and everything is logged, so there is an audit trail of what was done and why.
How long before we see something real?
A working prototype on your own content inside the first few weeks is normal. Getting it from prototype to something the business can rely on is the longer part, and that is mostly evaluation, permissions and edge cases.