We help businesses across multiple industries build scalable, secure and future-ready digital products. Our team combines deep technical expertise with industry knowledge to deliver custom software solutions that drive growth.
Let's TalkAnyone can call an LLM API. Getting it to answer correctly, cheaply and safely — a thousand times a day, in front of real users — is the actual work. We build generative AI features end-to-end: prompt and RAG for speed, fine-tuning where it earns its keep, guardrails on top, and cost tracking from day one.
System
You are a support agent for a fintech app. Answer only from the knowledge base. Refuse if unsure.
User
"How do I freeze my card while travelling?"
Assistant · streaming
142 tok/sOpen the app, tap Cards, then Freeze. You can unfreeze anytime from the same screen. While frozen, only standing instructions like subscriptions will still go through
Grounded
RAG ✓
Latency
540ms
Cost / call
$0.0031
Guardrails on · hallucination check passed
Text, images, code, audio, video or agents — the same discipline applies: get the data right, evaluate honestly, deploy carefully, watch the bill.
Most requested
Support copilots, document Q&A, contract review, research assistants. We ground every answer in your own content with vector search, reranking and citation, so the model stays on-topic instead of inventing things.
Product shots, ad variants, style transfer, upscaling. SDXL, Flux or your own fine-tune.
Internal dev assistants trained on your codebase, style guide and review comments.
Voice assistants, dubbing, call summaries and multilingual TTS.
Text-to-video ads, explainers, avatar spokespersons, auto-editing.
Agents that read tickets, run tools, fill forms and hand back to a human when unsure — with logging and approval gates.
Most teams jump to fine-tuning too early. Nine times out of ten, better prompts and retrieval solve the problem for a fraction of the effort.
System messages, few-shot examples, structured output (JSON), chain-of-thought. Cheapest to change, fastest to test.
Vector store over your own docs, PDFs, tickets, databases. The model answers from them, with citations.
Teach the model your style, tone and edge cases. Worth it once prompts and retrieval have plateaued.
The chat interface is the easy part. These are the pieces that make it correct, cheap and safe in production.
Classify the query, pick the right tool or model, decide if it needs retrieval.
Hybrid search (BM25 + vectors) over your knowledge base, then rerank the top matches.
Trim the chunks, keep the citations, fit inside the token budget.
Streamed response from the chosen LLM, with prompt caching and fallback if the primary is slow.
PII redaction, jailbreak check, hallucination score, refusal rules. Answer blocked if any fail.
Every call logged with latency, cost and quality score. Weekly eval runs on a golden set.
We're not tied to one vendor. Most production stacks use two or three, picked per task, based on cost, latency and accuracy.
| Use case | Best fit | Why | Cost band |
|---|---|---|---|
| Chat & complex reasoning | GPT-4o / Claude Sonnet | Best accuracy on multi-step tasks | $$$ |
| High-volume extraction | GPT-4o-mini / Haiku | Reliable JSON, ~10× cheaper | $ |
| Brand-tuned writing | Fine-tuned Llama 3 / Mistral | Consistent voice, self-hosted | $$ |
| Embeddings & search | text-embedding-3 / E5 / BGE | Fast, cheap, multilingual | ¢ |
| On-prem / regulated | Llama 3 / Mistral / Qwen | Data never leaves your VPC | $$$ |
| Image generation | SDXL / Flux / DALL·E | Depends on style & speed | $$ |
LLMs don't have a sense of truth. Guardrails and evaluation are how we stop them from confidently making things up in front of your customers.
PII redaction & prompt scrubbing
Phone numbers, emails, IDs removed before they leave your network — and re-inserted in the response.
Hallucination scoring
Answer checked against retrieved sources. Below a threshold → falls back to "I don't know" or a human.
Jailbreak & prompt-injection defence
Tools and rules isolate user input from system instructions. Tool calls are policy-checked.
Golden-set evaluation
A fixed set of test questions with known-correct answers, run on every prompt or model change.
Eval dashboard
Hallucination rate, refusal rate and cost per answer — reviewed every week
Deflect tier-1 tickets, draft replies, auto-tag and route the rest. Live with citations.
Copy variants, ad creatives, SEO drafts, and image generation for A/B testing at scale.
Invoice parsing, contract review, clause extraction and audit summarisation.
"Ask your data" tools that turn natural-language questions into SQL and charts.
Recommendation blurbs, onboarding copy and help content tailored per user.
Internal code assistants, PR summarisers and incident-postmortem writers tuned to your repo.
Build cost depends on the use case and how much of your data needs preparing. Run cost is mostly tokens — we design for that on day one.
A single chatbot or extraction task, working on your real data with an eval set.
RAG or fine-tuned models, guardrails, streaming UI, cost dashboards and ongoing eval.
Shared vector store, prompt library, model gateway, evaluation harness and governance.
Token spend is the new cloud bill. We budget it like one: prompt caching for repeated context, cheaper models for easy calls, batching where latency allows, and a dashboard that shows cost per feature, per customer and per day. If the bill is trending wrong, we tell you before your finance team does.
A couple of lines on the use case, the data you have, and where the output will live. We'll reply within a working day with a few questions and a rough build + run-cost estimate.
sales@softfix.in
Phone
+91 81890 10044
Address
1st Floor, CP/140, Viraj Khand-4, Gomti Nagar, Lucknow, Uttar Pradesh 226010
to help our experts understand your business objectives and create your customized plan.
