Language models

LLM and generative AI

Large language models put to work on real problems: assistants grounded in your knowledge, agents that complete multi step tasks, and generation pipelines with the evaluation, guardrails and cost control to run them in production.

Discuss your project See how it works

3 to 6 weeksto a grounded assistant running against your content

60 to 90%lower cost per task with routing, caching and small models

Every releasescored on an evaluation set before it ships

Your choiceof hosted API or a private model in your own environment

Beyond the demo

A convincing prototype and a dependable product are very different things

It takes an afternoon to wire a chat box to a language model and get something impressive. It takes real engineering to make it accurate on your content, safe with your users, affordable at your volume and stable as models change underneath you.

We build the parts that are missing from the demo: retrieval that grounds answers in your data, evaluation that catches regressions, guardrails that keep output on topic and on policy, observability that shows what users actually ask, and a cost model that survives contact with production traffic.

The model provider is a detail, not the architecture. We design so you can move between hosted APIs and private models, mix a large model with cheaper small ones, and keep working when a provider deprecates a version.

01
Retrieval augmented generation
Answers grounded in your knowledge, with citations
A language model on its own guesses from training data that is generic and out of date. Retrieval augmented generation puts your documents, tickets, wiki, code and records in front of the model at question time, so answers reflect your reality and link back to the source.
We build the full pipeline: ingest and parse your content, split it in a way that preserves meaning, index it with hybrid search, rerank the candidates, assemble a context that fits the window and prompt the model to answer only from what it was given, or to say it does not know.
Freshness is handled with incremental sync and permissions are respected at query time, so a user only ever retrieves what they are allowed to see.
- Layout aware parsing for PDFs, spreadsheets, slides and HTML
- Hybrid vector and keyword search with cross encoder reranking
- Per user permission filtering applied before retrieval
- Inline citations and a refusal path when confidence is low
02
Agents and workflows
Systems that take an instruction and complete the task
When a job needs several steps, a lookup here, a calculation there, a form filled in, an email drafted, an agent plans the sequence, calls tools with typed inputs and outputs, checks its own progress and asks a person to approve anything sensitive.
We keep agents on a short leash: a bounded set of tools, step limits, budgets, and a clear log of every decision and tool call so behaviour can be audited and improved.
For most business processes a constrained workflow with a few well chosen model calls beats an open ended agent. We use the simplest design that does the job reliably.
- Typed tool interfaces with validation and retries
- Step and cost budgets with human approval gates
- Full trace of plans, tool calls and results
- Deterministic workflow where a free agent is not needed
03
Fine tuning and model strategy
The right model for each job, adapted where it pays off
Prompting a large general model is the right start. When a task is narrow, high volume or needs a specific style or format, a smaller fine tuned model can match quality at a fraction of the cost and latency.
We fine tune with parameter efficient methods on curated examples, distil from a larger model where useful, and route each request to the cheapest model that meets the quality bar, falling back to a stronger one when needed.
- LoRA and QLoRA fine tuning on curated datasets
- Distillation from a strong model to a small deployable one
- Request routing by difficulty, with escalation
- Prompt caching and response caching to cut repeat cost
04
Evaluation and guardrails
Know it works before users do, and keep it in bounds
Every change to a prompt, a model or the retrieval setup is scored against an evaluation set built from real questions and expected answers, with metrics for correctness, grounding, format and tone. A release that regresses is blocked.
At run time, input and output filters check for prompt injection, personal data, unsafe content and off topic requests. Output is validated against a schema when structure matters, and the system refuses rather than guesses when it should.
- Golden set and rubric based scoring, human and model graded
- Regression gates in the deployment pipeline
- Prompt injection and jailbreak filtering
- Schema validated output with automatic repair or refusal

What a production LLM system needs

These are the components that separate something you can launch from something you can only screenshot.

Conversation memory

Short and long term memory with summarisation, so a session stays coherent without blowing the context window or the budget.

Structured output

Function calling and JSON schema enforcement so the model returns data your code can use directly, with validation and repair.

Observability

Every request traced with prompt, retrieved context, tokens, latency, cost and user feedback, so you can see and improve real usage.

Access and rate control

Per user and per tenant quotas, abuse detection and spend caps, so one client or one loop cannot run up the bill.

Provider abstraction

One internal interface over multiple model providers, so you can switch, mix or add models without touching product code.

Content sync

Incremental indexing keeps the knowledge base current within minutes of a source document changing.

What clients build with LLMs

Applications where generative AI is delivering measurable value today.

Support assistants

Grounded answers from your help centre and past tickets, drafting replies for agents or handling tier one directly with a clean handover.

Knowledge search for staff

A single place for employees to ask questions across policies, wikis, contracts and systems, with source links and permissions respected.

Document drafting and review

First drafts of proposals, reports and responses, plus review that flags risk, missing clauses and inconsistencies against your standards.

Extraction and classification

Turning free text emails, notes and forms into structured records routed into your workflows, with confidence scores.

Developer tooling

Assistants over your codebase and docs for onboarding, code review help and answering how our system works questions.

Analytics in plain language

Ask a question, get a governed query and a chart, with the metric definitions coming from your semantic layer.

How an LLM project runs

A route that proves accuracy and cost on your data before you scale it to everyone.

Scope and baseline

We pick one use case, gather real example inputs and expected outputs, and build the first evaluation set. That set defines done.

Build the thin slice

Retrieval or workflow, a first prompt, structured output and guardrails, scored against the evaluation set and tried by a small group of real users.

Harden and optimise

Routing, caching, fine tuning where it pays, observability, rate limits and a load test at your expected volume with a firm cost per task.

Roll out and operate

Staged release with monitoring, a feedback loop from real conversations into the evaluation set, and a schedule for model and prompt updates.

Models and tooling we use

Models

ClaudeGPTGeminiLlamaMistralQwen

Retrieval and orchestration

pgvectorQdrantElasticsearchLangGraphLlamaIndexHaystack

Serving and ops

vLLMOllamaLiteLLMLangfuseRagaspromptfoo

Questions about LLM and generative AI

Will the model make things up?

A raw model will. A well built system reduces it to a small, measured rate. Retrieval forces answers to come from your sources, prompts instruct the model to refuse when the context does not contain an answer, and every claim can carry a citation. Evaluation tracks the grounding rate on every release so you know where it stands.

Do we have to send our data to an external provider?

No. We can run open weight models such as Llama or Mistral in your own cloud or on premise, so no data leaves your environment. Where a hosted API is used, we use zero retention configurations and send only the minimum context needed. The architecture lets you change this decision later without a rebuild.

How do you control the cost?

Several ways, each measured. Route easy requests to small cheap models and only escalate hard ones. Cache prompts and repeated answers. Trim retrieved context to what is needed. Fine tune a small model for high volume narrow tasks. Set per user and per tenant spend caps. Clients typically see cost per task fall by a large margin between the prototype and production.

What happens when the model provider changes or retires a version?

Because product code talks to one internal interface rather than a specific provider, swapping models is a configuration change plus an evaluation run. We re score the candidate against your golden set, compare quality and cost, and switch when it passes. Your application does not change.

How do you stop prompt injection and misuse?

Input filtering for injection and jailbreak patterns, retrieved content treated as untrusted data rather than instructions, output filtering for personal data and unsafe content, schema validation on structured responses, and per user rate and spend limits. Sensitive actions require human approval. The guardrail set is tested as part of evaluation.

Can it work with our existing systems?

Yes. The assistant or agent calls your APIs through typed tools, retrieval connects to your document stores and databases, and results are written back into the tools your teams already use. It is built as a service your software calls, not a separate island.

Bring the use case you keep hearing about

The assistant, the agent, the drafting tool. We will scope a version that is accurate on your data, safe for your users and affordable at your volume.

Start a project Spykra Technologies UK Ltd, London and Mumbai.