Generative AI,
shipped as software.
Production generative AI, RAG, agents, fine-tuning, structured extraction, evaluations and guardrails, engineered with cost, latency and safety envelopes designed in from day one.
A full generative-AI practice, engineered end to end.
From LLM applications and retrieval systems through agents, fine-tuning, evaluation and guardrails, our practice covers the full delivery surface of production generative AI.
LLM application engineering
End-to-end generative AI applications built as first-class products, model orchestration, structured prompting, tool use, streaming and state, engineered the way any production system is engineered.
Retrieval-augmented generation
RAG pipelines that ground models in your own knowledge, chunking, embedding, hybrid retrieval, re-ranking, citation and eval, tuned to your domain rather than lifted from a blog post.
AI agents & tool use
Agents that plan, call tools and hand off, with structured memory, deterministic checkpoints and human-in-the-loop controls calibrated to the risk of getting it wrong.
Fine-tuning & adaptation
Supervised, preference and instruction fine-tuning on foundation and open-weights models, with data curation, ablation and eval baked in, so you know the model got better and why.
Prompt engineering & optimisation
Prompt design as an engineering discipline, versioned, tested, cached and measured, not as a one-off document handed off to product. Includes systematic prompt-optimisation loops against real eval sets.
Structured extraction & generation
Reliable extraction of structured data from documents, transcripts and unstructured text, with schemas, validators and repair loops, so downstream systems can trust the output.
Multimodal systems
Vision, audio and document intelligence built on top of multimodal models, OCR, layout understanding, image generation and speech, evaluated on the tasks you actually need, not the demos.
Embeddings & semantic search
Embedding models, vector stores and hybrid search, designed against real query distributions, with re-ranking and evaluation, so retrieval quality is a number your team can move.
Evaluation & red-teaming
Domain-specific eval harnesses, LLM-as-judge with calibration, adversarial red-teaming and regression suites, treated as first-class engineering artefacts that gate deploys.
Guardrails & safety
Input and output guardrails, PII redaction, jailbreak defence, tool-use scoping, content policy and hallucination checks, engineered proportional to risk and audited end-to-end.
Cost, latency & serving
Serving stacks tuned against defined cost-per-outcome and latency budgets, routing between providers, batching, caching, speculative decoding and self-hosted inference where it earns its keep.
Responsible & compliant GenAI
Model documentation, bias assessment, provenance and evidence trails aligned to the EU AI Act, ISO/IEC 42001 and NIST AI RMF, produced alongside delivery, not bolted on before launch.
Prototype fast. Ship what survives an eval.
A delivery model built for the specific shape of GenAI work, fast prototyping against real data, evaluation as the gate to production, and operations designed in from day one.
Frame
A working session to sharpen the use case, define what 'good' looks like, and audit the data, systems and constraints. The output is a phased plan with an eval set already sketched.
Prototype
A fast, rigorous prototype against real data and a real eval, not a demo. We size the model, the pattern (RAG, fine-tune, agent) and the cost envelope before anything gets built to last.
Productionise
Iterative delivery of the full stack, retrieval, orchestration, guardrails, observability, human-in-the-loop, integrated with your platform and pipelines. Deploys are gated by evals, not opinions.
Operate
Drift, cost, latency and quality monitored as ongoing engineering disciplines. Prompts, models and retrieval are versioned; regressions are caught in CI. Knowledge transfer is designed in from day one.
A pragmatic generative-AI toolchain.
Model, framework and platform selection is a working decision, driven by the use case, your existing landscape and long-term economics, not by preference.
PythonOOpenAIAAnthropic
LangChainLLlamaIndex
Hugging Face
PyTorchVvLLMPPineconeWWeaviatePpgvector
Elasticsearch
Redis
KafkaLLangSmith
MLflow
Ray
Kubernetes
AWS Bedrock
Azure OpenAI
Vertex AI
Databricks
Snowflake
PostgresProgrammes delivered on this practice.
What prospective clients ask us first.
If your question isn't addressed here, please reach us at hello@tekansa.com.
01RAG or fine-tuning, how do we decide?
The two solve different problems and are frequently combined. RAG is the right answer when the knowledge changes, needs to be cited, or has to stay behind an access boundary, you retrieve the truth at inference time and ground the model in it. Fine-tuning is the right answer when the task itself needs to change, tone, structure, reasoning style, latency profile, or when a smaller adapted model becomes cheaper than a large hosted one. We recommend per use case, based on data availability, refresh cadence, cost profile and the accuracy the workflow can tolerate.
02How do you evaluate GenAI systems when outputs are open-ended?
Evaluation is designed in from day one. We build task-specific eval sets from your own data, use a combination of programmatic checks, LLM-as-judge with calibration against human graders, and adversarial red-team sets. Evals run in CI, gate deploys, and generate the evidence trail. For anything user-facing we also stand up online evaluation, thumbs, edits, escalations, so the eval set keeps up with reality rather than freezing on day one.
03How do you handle hallucination, prompt injection and other GenAI-specific risks?
Layered defence, proportional to risk. Input guardrails cover PII, prompt-injection patterns and out-of-scope requests. Retrieval, structured output and citation reduce hallucination surface. Output guardrails run policy checks, factuality checks against sources, and repair loops. Tool use is scoped, audited and reviewed where the blast radius warrants it. Nothing goes to production without a red-team pass, and the guardrail stack itself is versioned and evaluated like any other engineering artefact.
04How do you manage cost and latency in production GenAI?
As real engineering budgets, not aspirations. Each surface gets a cost-per-outcome and p95 latency envelope, and the serving stack is chosen against those numbers, routing between providers, caching, batching, streaming, smaller distilled models where they meet quality, self-hosted inference where the volume earns it. We instrument tokens, cache hit rates and per-request cost from day one, so trade-offs are visible and negotiable rather than surprises at the invoice.
05How do you choose between hosted models, open-weights and self-hosting?
It's a working decision, not a religious one. Hosted frontier models are usually the right start, they compress time-to-value and let us pin down the target. As the use case stabilises, we test smaller and open-weights models against the same eval set; where they hit quality and change the economics or the data-residency story, we move. Self-hosted inference gets pulled in when volume, latency, compliance or IP constraints make it the honest answer, never as a default.
06How do you approach responsible and compliant GenAI?
Documentation, evaluation and evidence trails are produced as part of delivery, not appended to it. We align to the EU AI Act's risk-tier framework, ISO/IEC 42001 and NIST AI RMF, and adapt to sector regimes, HIPAA, PCI, DORA and equivalents, where they apply. Bias and safety evaluations, model cards, data-lineage records and human-oversight designs are engineered artefacts on the same footing as tests, and they ship with the system rather than trailing it.
Let's discuss your generative-AI programme.
Share a brief overview of the use case and we'll arrange an introductory conversation with the relevant practice leadership. Every enquiry receives a direct response.